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Conference Book

Conference Book — Version 1.0
Sixth International Conference on Continuous Optimization
Berlin, Germany, August 5–8, 2019
Michael Hintermüller (Chair of the Organizing Committee)
Weierstrass Institute for Applied Analysis and Stochastics (WIAS)
Mohrenstraße 39, 10117 Berlinc July 16, 2019, WIAS, Berlin
Layout: Rafael Arndt, Olivier Huber, Caroline Löbhard, Steven-Marian Stengl
Print: FLYERALARM GmbH
Picture Credits
page 19, Michael Ulbrich: Sebastian Garreis
page 26, “MS Mark Brandenburg”: Stern und Kreisschiffahrt GmbHICCOPT 2019
Sixth International Conference on Continuous Optimization
Berlin, Germany, August 5–8, 2019

Program Committee
Michael Hintermüller (Weierstrass Institute Berlin,
Humboldt-Universität zu Berlin, Chair)Hélène Frankowska (Sorbonne Université)
Jacek Gondzio (The University of Edinburgh)
Zhi-Quan Luo (The Chinese University of Hong Kong)
Jong-Shi Pang (University of Southern California)
Daniel Ralph (University of Cambridge)
Claudia Sagastizábal (University of Campinas)
Defeng Sun (Hong Kong Polytechnic University)
Marc Teboulle (Tel Aviv University)
Stefan Ulbrich (Technische Universität Darmstadt)
Stephen Wright (University of Wisconsin)

Organizing Committee
Michael Hintermüller (Weierstrass Institute Berlin,
Humboldt-Universität zu Berlin, Chair)
Mirjam Dür (Universität Augsburg)

Welcome Address
On behalf of the ICCOPT 2019
Organizing Committee, the Weierstrass Institute for Applied Analysis
and Stochastics Berlin, the Technische Universität Berlin, and the Mathematical Optimization Society, I welcome you to ICCOPT 2019, the 6thInternational Conference on Continuous Optimization.
The ICCOPT is a flagship conference of the Mathematical
Optimization Society. Organized every three years, it covers all
theoretical, computational, and practical aspects of continuous
optimization. With about 150 invited and contributed sessions
and twelve invited state-of-the-art lectures, totaling about 800
presentations, this is by far the largest ICCOPT so far. Besides
that, the ICCOPT 2019 includes a Summer School (August
3–4), a poster session, and an exhibition. The finalists for theBest Paper Prize in Continuous Optimization will present theircontributions, and the prize will be awarded at the conference
banquet.
More than 900 participants from about 70 countries all over
the world will learn about the most recent developments andresults and discuss new challenges from theory and practice.
These numbers are a clear indication of the importance ofContinuous Optimization as a scientific discipline and a key
technology for future developments in numerous application
areas.

Gabriele Eichfelder (Technische Universität Ilmenau)
Carsten Gräser (Freie Universität Berlin)
René Henrion (Weierstrass Institute Berlin)
Michael Hinze (Universität Hamburg)
Dietmar Hömberg (Weierstrass Institute Berlin,
Technische Universität Berlin)
Florian Jarre (Universität Düsseldorf)
Christian Kanzow (Universität Würzburg)
Caroline Löbhard (WIAS Berlin)
Volker Mehrmann (Technische Universität Berlin)
Carlos N. Rautenberg (Weierstrass Institute Berlin,
Humboldt-Universität zu Berlin)
Volker Schulz (Universität Trier)Fredi Tröltzsch (Technische Universität Berlin)
Michael Ulbrich (Technische Universität München)
Stefan Ulbrich (Technische Universität Darmstadt)

World-renowned cultural and research institutions, a thriving
creative scene and a rich history make Berlin a popular place
to live, work and travel. During a river cruise, participants of
the Conference Dinner are invited to enjoy Berlin’s historic city
center and main sights while having dinner with colleagues
from all over the world. I hope that you will also find the time
to take a look around Berlin on your own, to obtain a feelingfor the vibrant lifestyle, and to explore the many attractions of
this wonderful city.
Finally, I would like to express my sincere appreciation to
the many volunteers who made this meeting possible. I wish
to acknowledge, in particular, the members of the programcommittee, the cluster chairs, and the many session organizers
for setting up the scientific program. My sincere thanks go
to the members of the organizing committee and everyone
involved in the local organization — the system administrators,
secretaries, student assistants, PhD students, and postdocs —for the many days, weeks and even months of work.
I wish you all a pleasant and memorable ICCOPT 2019,
and a lot of exciting mathematics in the open-minded andinternational atmosphere of Berlin.
Berlin, August 2019

Michael Hintermüller

4

Sponsors

The ICCOPT 2019 is Supported byAcademic Sponsors

Corporate Sponsors

Sponsors

Contents

Overview of Events

Sponsors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Campus Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Floor Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
General Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Social Program/City Tours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Program Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Special Events. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12
Exhibition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Posters Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Posters Abstracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Conference Dinner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Parallel Sessions Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Parallel Sessions Abstracts Mon.1 11:00–12:15 . . . . . . . . . 39
Parallel Sessions Abstracts Mon.2 13:45–15:00 . . . . . . . . . 50
Parallel Sessions Abstracts Tue.1 10:30–11:45 . . . . . . . . . . 62
Parallel Sessions Abstracts Tue.2 13:15–14:30 . . . . . . . . . . 73
Parallel Sessions Abstracts Tue.3 14:45–16:00 . . . . . . . . . . 85
Parallel Sessions Abstracts Tue.4 16:30–17:45 . . . . . . . . . . 96
Parallel Sessions Abstracts Wed.1 11:30–12:45 . . . . . . . . 107
Parallel Sessions Abstracts Wed.2 14:15–15:30 . . . . . . . . 119
Parallel Sessions Abstracts Wed.3 16:00–17:15 . . . . . . . . 131
Parallel Sessions Abstracts Thu.1 09:00–10:15. . . . . . . . .143
Parallel Sessions Abstracts Thu.2 10:45–12:00. . . . . . . . .154
Parallel Sessions Abstracts Thu.3 13:30–14:45. . . . . . . . .164
Index of Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

Registration

5

The ICCOPT Registration Desk is located in the lobby of theMain Building, see the floor plan on page 7. The opening times
are listed on page 8.

Welcome Reception, Sunday, 17:00–20:00
Everyone is invited to join the welcome reception in the Lichthof.
Drinks and snacks will be offered, and you can already collect
your conference materials at the ICCOPT Registration Desk.

Opening Ceremony, Monday, 09:00
The Opening of the ICCOPT with welcome addresses and an
artistic sand show takes place in H 0105 (Audimax).

Best Paper Award in Continuous Optimization
The winner will be determined after the Best Paper Session
on Monday, 16:20–18:20 in H 0105 (Audimax), see page 13.
The prize will be awarded at the conference dinner, see below.

Plenary and Semi-Plenary Talks
Featured state-of-the-art talks are given by twelve distinguishedspeakers, see page 12.

Memorial Session, Tuesday, 16:30–17:45
A special session in honor of Andrew R. Conn, who passed
away on March 14, 2019 takes place in H 0105 (Audimax), seepage 15.

Parallel Sessions

Download the conference app athttps://iccopt2019.berlin/app

More than 800 talks are given in almost 170 invited and contributed sessions, see the session overview on page 27 and thedetailed session description starts on page 39. All alterationsin the scientific program will be displayed on a board near the
information desk in the lobby of the Main Building.

Poster Session
On Monday, a poster session with snacks and drinks is plannedin the Lichthof, see page 21.

Conference Dinner
Download the online version of this book at
https://iccopt2019.berlin/conference_book

On Tuesday, the conference dinner will take place on the boat
“MS Mark Brandenburg” on the river Spree. The cruise willdepart at 19:00 (boarding starts at 18:30) and last four hours.
More details can be found on page 8.

Board Meetings
The following board meetings will take place:
SIOPT
COAP
MOR

Tuesday,
August 6, 12:00
Wednesday, August 7, 13:00
Wednesday, August 7, 13:00

H 3005,H 3005,
H 2036.

Snacks and beverages will be served. Please note that those
events are not open to the public.

6

Campus Map

Campus Map

r

ufe

tein

s
Ein

Ma
rch
str

aß
e

Conference Dinner
departure at Gotzkowskybrücke
20 min from TU Main Building, or
12 min with Bus 245 from Ernst-Reuter-Platz to Franklinstr.

Straße des 17. Juni

Urban Rail Station
Tiergarten

Underground Station
Ernst-Reuter-Platz

Fas
a

nen
s

tra

ße

TU Berlin
Main Building

rde
Ha
nb

Train Station
Zoologischer
Garten

a
str

erg

Mensa

ße

Train Station
Zoologischer
GartenFloor Plan

Floor Plan

Lobby

Lichthof

ICCOPT Registration Desk

Coffee Break Area

Cloakroom

ICCOPT Registration Desk (page 8)

Welcome Reception

H 2036 and H 3008 can be reserved for meeting, calls, etc. (page 8)

Exhibition

Paramedic station

Poster Session

Room entrance

Coffee and snacks

H3
H 30 H 3008 007
1
2
H 30
13

H 30

H 30
05

4

H 300

06

2
H 300

H 30

25

3rd

floo
r
3
H 201
3

H 203
2
H 203

H 20

H 20

38

36

3

H 205

2nd

floo
r
2
H 101
9

H 102
8
H 102

WC

WC

Lichthof

8

H 105

1st

H 0104floor

Audimax
H 0105
GalleryWC

Foyer

H 0104Gro

H 01 H 0107
06

Lobby

und

floo
r

WC

Audimax
H 0105Main

H 01
H 01 H 0111 12
10ceEntran

Juni
s 17.
e
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Straß

7

8

General Information

General Information
ICCOPT Registration Desk. The ICCOPT Registration
Desk is located in the lobby (Foyer, ground floor, left) of the
TU Berlin Main Building. The opening times are as follows:
Sunday, August 4:
Monday, August 5:
Tuesday, August 6:
Wednesday, August 7:
Thursday, August 8:

17:00–20:00
07:00–20:00
08:00–18:00
08:00–18:30
08:00–17:30

Getting around by public transport. The conference badge
allows you to use all public transport in and around Berlin (zoneABC) during the conference from August 5 to August 8. In
order to identify yourself, you need to carry along your passport
or national ID card. The “FahrInfo Plus” app for Android and
iOS will help you move inside the city. For more information
on public transport in Berlin, visit the BVG webpage
https://www.bvg.de/en/

Registration fee. Your registration fee includes admittanceto the complete technical program and most special programs.
The following social/food events are also included: Opening
ceremony including reception on Sunday evening, poster session
on Monday evening, and two coffee breaks per day.
Badges required for conference sessions. ICCOPT badges
must be worn at all sessions and events. Attendees withoutbadges will be asked to go to the ICCOPT Registration Desk
to register and pick up their badges. All participants, including speakers and session chairs, must register and pay theregistration fee.
Conference dinner tickets. The conference dinner on Tuesday evening is open to attendees and guests who registeredand paid in advance for tickets. Tickets are included in yourregistration envelope. There may be a limited number of tickets
available on site. You may go to the ICCOPT Registration
Desk to enquire. Tickets are 65 EUR.
For further details, see page 26.
Info board. Changes at short notice and other important
items will be displayed on an info board next to the ICCOPT
Registration Desk.
Rooms for spontaneous meetings. The following rooms
are available for spontaneous meetings, phone calls, or other:
H 2036:H 3008:

Monday–Thursday
Monday, Tuesday, and Thursday

A reservation form will be available outside of each room.
Internet access.
If your home institution participates ineduroam and you have an account, you can directly connect tothe eduroam Wi-Fi. Otherwise, a guest account, for using the
Wi-Fi network at the TU Berlin, is included in your conferencematerials. If you need help, please visit the Wi-Fi helpdesk,
which is located in the lobby of the Main Building.
Cloakroom. Participants are asked to carefully look after
their belongings for which the organizers are not liable. You
will find a cloakroom next to the ICCOPT Registration Desk.
Snacks and coffee breaks. Coffee, tea, and beverages
are served during all breaks at the conference venue. Waterand fruits will be available over the whole conference period.
Moreover, various cafeterias are located in or close to the Main
Building of TU Berlin.
Food. The Mensa of the TU Berlin offers plenty of opportunities for lunch at moderate prices. Payment is only permittedwith a “Mensa card”: it will be possible to get one on the conference premises. In the vicinity of the TU Berlin, there is also
a variety of restaurants from fast food to gourmet restaurants.
For the daily lunch break, please consult the Restaurant Guide
for a list of nearby cafeterias and restaurants. You will find thisguide in your conference bag and on the ICCOPT USB flash
drive.

Shopping. Most supermarkets are open from 08:00 to 22:00
in the city. The opening times of cafés, restaurants and small
shops varies, but the vast majority should be accessible between
10:00 and 18:00. Note that most shops are closed on Sundays.
Since Berlin is a vibrant city, a variety of different kinds ofshops can be found around the TU Berlin. Indeed, the famous
shopping center KaDeWe is only two subway stations awayfrom the conference venue. Prices can drastically vary fromstore to store.
Emergency numbers. On the ground floor, next to thelobby and the Audimax, a paramedic station will be manned
during the conference. In case of an emergency keep the following important phone numbers in your mind.
Police:Ambulance:
Fire brigade:

110
112
112

Questions and information. The organizers, staff of theregistration desk, and student assistants will be identifiable bycolored name tags and green T-shirts. Please contact them if
you have any questions. Do not hesitate to enquire about allnecessary information concerning the conference, orientation in
Berlin, accommodations, restaurants, going out, and cultural
events at the information desk which is located in the lobby of
the Main Building.

Social Program/City Tours

9

Social Program/City Tours
Tour 1: Monday, August 5, 14:00–16:00
Discover the historic heart of Berlin

Tours must be booked online athttps://iccopt2019.berlin/tours.html

Every street in the center of Berlin has its history, much of it
no longer visible: On this walk you will meet the ghosts andmurmurs of Prussians and Prussian palaces, Nazis and Nazi
architecture, Communists and real, existing socialist architecture, as well as visit some sites of the present.
Meeting point: Berliner Abgeordnetenhaus, Niederkirchnerstraße (near Potsdamer Platz)
End: Unter den Linden/ Museumsinsel

Tour 2: Tuesday, August 6, 14:00–16:00

All sightseeing tours are held in English and are offered by
StattReisen Berlin GmbH. The following holds for all tours:

Where was the Wall?
Price: 10 EUR
Berlin: The heart of the cold war. So little is left. Walking theformer “deathstrip” between Checkpoint Charlie and Potsdam
Square, listen to the stories of how a city was violently split in
1961, how one lived in the divided city, how some attempted
to escape from the east, how the wall fell in 1989, and memory
today.
Meeting point: U-Bhf. Stadtmitte, at the platform of U6 (notU2!)
End: Potsdamer Platz

Tour 3: Wednesday, August 7, 14:00–16:00
Kreuzberg — Immigrant City
From Immigrants to Inhabitants
Huguenots, Viennese Jews, Silesians: Immigration has always characterized the history of Berlin. One of the mostvibrant Berlin districts today consists of Turkish, Swabian, Polish and other “Kreuzbergers”. Encounter churches, mosques,
synagogues, and a variety of ethnic food. Social tensions become visible here, too. Is Kreuzberg an example of a modern
multi-cultural society made up of many (sub-)cultures? Is the
district truly a melting pot, or do its inhabitants simply co-exist
in parallel worlds?
Meeting point: Kottbusser Tor/ Corner Admiralstraße, beforethe ”is-bank” (U-Bhf. Kottbusser Tor)End: Oranienplatz

Tour 4: Thursday, August 8, 14:00–16:00
Where was the Wall?
Berlin: The heart of the cold war. So little is left. Walking theformer ”deathstrip” between Checkpoint Charlie and Potsdam
Square, listen to the stories of how a city was violently split in
1961, how one lived in the divided city, how some attempted
to escape from the east, how the wall fell in 1989, and memory
today.
Meeting point: U-Bhf. Stadtmitte, at the platform of U6 (notU2!)
End: Potsdamer Platz

Payment: cash only, on site to the guide
Every participant needs to register herself/himself
Group size: min. 6, max. 25; if a tour has more than 25
bookings, the participants are split into groups.
Booking deadline: July 28, 2019
The tour operator offers additional tours: you may have alook at their offering at
https://www.stattreisenberlin.de/english/

10

Speaker and Chair Information

List of Clusters and Cluster Chairs

General guidelines

Applications in Economics, Energy, Engineering and Science APP

Date, timeslot, and location of the parallel sessions arelisted as an overview on page 27. The detailed description
starts on page 39.
For each session, helpers will be present to help with potential technical issues. Speakers and chairs can approachthem for help with conference equipment. For generalquestions about the conference, consult the conference
web page https://iccopt2019.berlin or address the
ICCOPT Registration Desk located in the lobby.
Speaker guidelines
Please bring a USB flash drive with a copy of your presentation with you.
Please arrive at least 15 minutes before the beginning of
the session. This is needed as a reaction time in case of
unforeseen complications.
To guarantee a smooth running of the conference activities,
we ask to ensure that the presentation lasts 20 mins, so
as to leave 5 mins for questions and change of speakers.
In justified exceptional cases, it is possible to connect yourlaptop to the conference equipment. Under these circumstances, make sure to bring a power adapter compatible
to the German power grid (230 V, 50 Hz). All session
rooms are equipped with a video projector with HDMI and
VGA input. If your device does not support that format,
please have the required adapter at hand. However, we
do recommend the use of a USB flash drive instead.
Chair guidelines
The chairs introduce the speakers, ensure the complianceof the speaking time limit and guarantee the smoothrunning of the session. Each presentation is supposed tolast 20 minutes, the discussion together with the changeof speaker to last 5 minutes.
Please arrive at least 15 minutes before the beginning of
the session. This is needed as a reaction time in case of
unforeseen complications.
We ask the chairs to notify the ICCOPT Registration
Desk about short notice changes and cancellations.

Lars Schewe (FAU Erlangen–Nürnberg, Germany)Peter Schütz (NTNU, Norway)
Golbon Zakeri (U Auckland, New Zealand)
Big Data and Machine Learning

BIG

Gitta Kutyniok (TU Berlin, Germany)
Kim-Chuan Toh (NUS, Singapore)
Complementarity and Variational Inequalities

VIS

Michael Ferris (U Wisconsin–Madison, USA)
Thomas Surowiec (U Marburg, Germany)
Conic, Copositive, and Polynomial Optimization

CON

Etienne de Klerk (Tilburg U, The Netherlands)
Luis Zuluaga (Lehigh U, USA)
Convex and Nonsmooth Optimization

CNV

Russell Luke (U Göttingen, Germany)
René Vidal (Johns Hopkins U, USA)
Derivative-free and Simulation-based Optimization

DER

Ana Luisa Custodio (NOVA Lisbon, Portugal)
Francesco Rinaldi (U Padova, Italy)
Global Optimization

GLO

Ernesto Birgin (USP, Brasil)
Man-Cho So (Chinese U Hong Kong, China)
Multi-Objective and Vector Optimization

MUL

Gabriele Eichfelder (TU Ilmenau, Germany)
Alexandra Schwartz (TU Darmstadt, Germany)
Nonlinear Optimization

NON

Coralia Cartis (U Oxford, UK)
Sandra Santos (U Campinas, Brazil)
Optimization Applications in Mathematics of Planet Earth PLA

Emil Constantinescu (Argonne National Laboratory, USA)

Pedro Gajardo (UTSM, Chile)
Optimization Implementations and Software

IMP

Todd Munson (Argonne National Laboratory, USA)
Dominique Orban (Polytechnique Montréal, Canada)
PDE-constrained Optimization and Optimal Control

PDE

Juan Carlos De los Reyes (EPN Quito, Ecuador)
Denis Ridzal (Sandia National Laboratory, USA)
Robust Optimization

ROB

Melvyn Sim (NUS, Singapore)
Wolfram Wiesemann (Imperial College, UK)
Sparse Optimization and Information Processing

SPA

Mario Figueiredo (Instituto Superior Técnico, Portugal)
Shoham Sabach (Technion, Israel)
Stochastic Optimization
René Henrion (Weierstrass Institute, Germany)
Shu Lu (U North Carolina, USA)

STO

Program Overview

11

Program Overview
Sun, Aug 4

Mon, Aug 5

Tue, Aug 6

Wed, Aug 7

Thu, Aug 8

09:00–09:30

09:00–10:00

09:00–10:00

09:00–10:15

Opening

Plenary Talk
Uday V. Shanbhag
page 14Plenary Talk
Tamara G. Kolda
page 15

09:30–10:30

Plenary Talk
Amir Beckpage 12
10:30–11:00

10:00–10:30

10:15–11:00

Coffee BreakSemi-Plenary Talks
Radu I. Boţ
Frauke Liers
page 16

10:30–11:45

Coffee Break
11:00–12:15

Parallel Sessions
Mon.1
page 27

12:15–13:45

Parallel Sessions
Tue.1
page 29

Parallel Sessions
Thu.1
page 36

10:15–10:45

Coffee Break
10:45–12:00

11:00–11:30

Coffee Break
11:30–12:45

Parallel Sessions
Thu.2
page 37

11:45–13:15

Lunch BreakParallel Sessions
Wed.1
page 33

12:00–13:30

Lunch Break
12:45–14:15

Lunch Break
13:15–14:30

13:45–15:00

Parallel Sessions
Tue.2
page 30

Parallel Sessions
Mon.2
page 28
14:45–16:00
15:00–15:30

Coffee Break
15:30–16:15

Semi-Plenary Talks
Darinka Dentcheva
Amr S. El-Bakry
page 12
16:20–18:20

17:00–20:00

Best Paper Session
page 13

Parallel Sessions
Tue.3
page 31

Lunch Break

13:30–14:45

14:15–15:30

Parallel Sessions
Thu.3
page 38

Parallel Sessions
Wed.2
page 34

Coffee Break
15:15–16:00

15:30–16:00

Coffee Break
16:00–16:30

16:00–17:15

Coffee Break
16:30–17:45

Parallel Sessions
Tue.4
page 32

Welcome Reception
18:20–20:30
19:00–23:00

Poster Session
page 21
Dinner

14:45–15:15

Semi-Plenary Talks
Frank E. Curtis
Piermarco Cannarsa
page 18
16:15–17:15

Parallel Sessions
Wed.3
page 35

Plenary Talk
Michael Ulbrichpage 19

17:30–18:15

17:15–17:30

Semi-Plenary Talks
Heinz Bauschke
Lieven Vandenberghe
page 17

Closing12Special Events

Special Events
Plenary Talk

Semi-Plenary Talk

Mon 09:30–10:30 H 0105

Mon 15:30–16:15 H 0105

Amir Beck, Tel Aviv University
Coordinate Descent Methods for Optimization Problems
with Sparsity Inducing Terms

Darinka Dentcheva, Stevens Institute of Technology
On Statistical Inference for Optimization Problems with
Composite Risk Functionals

Chair: Marc Teboulle

Chair: René Henrion

We consider nonconvex optimization problems in which coordinate-wise
type optimality conditions are superior to standard optimality conditions
such as stationarity. Among the models that we explore are those based onsparsity-inducing terms. We illustratethat the hierarchy between optimality
conditions induces a corresponding hierarchy between various optimization
algorithms.

We analyze risk models representing distributional characteristics of afunctional depending on the decision
maker’s choice and on random data.
Very frequently, models of risk are nonlinear with respect to the underlying
distributions; we represent them as
structured compositions.
We discuss the statistical estimationof risk functionals and present several
results regarding the properties of empirical and kernel estimators. We compare the performance ofthe estimators theoretically and numerically. Several popularrisk measures will be presented as illustrative examples. Furthermore, we consider sample-based optimization problems which
include risk functionals in their objective and in the constraints.
We characterize the asymptotic behavior of the optimal value
and the optimal solutions of the sample-based optimization
problems. While we show that many known coherent measuresof risk can be cast in the presented structures, we emphasizethat the results are of more general nature with a potentiallywider applicability. Applications of the results to hypothesis
testing of stochastic orders, portfolio efficiency, and others will
be outlined.

Special Events

Semi-Plenary Talk

Best Paper Session

Mon 15:30–16:15 H 0104

Mon 16:20–18:20 H 0105

Amr S. El-Bakry, ExxonMobil Upstream Research Comp
Optimization in Industry: Overview from the Oil and Gas
Energy Sector

Chair: Andrzej Ruszczyński

Chair: Peter Schütz
The oil and gas energy industry
is witnessing a renewed interest in
optimization technology across thevalue chain. With applications spanning exploration, development, production, and transportation of extracted
material, there is greater awareness ofthe value optimization technology canprovide. The questions of whether thisresurgence of optimization in this sector of the economy is sustainable or isa part of the cyclic technology interest remains to be seen.
This talk will provide an overview of some classical optimization models and other newer ones. Examples from thearea of production optimization will be presented. Links tomachine learning will be emphasized in multiple applications.
Opportunities and challenges will be discussed.

13

Four papers have been selected as finalists of the best paper
competition by the best paper committeeAndrzej Ruszczyński
Rutgers University, Chair
Dietmar Hömberg
Weierstrass Institute/TU Berlin
Philippe L. Toint
University of Namur
Claudia Sagastizábal
University of Campinas
The finalists will be featured in a dedicated session at ICCOPT
2019, and the prize winner will be determined after the finalist
session.
Amal Alphonse, Weierstrass Institute for Applied Analysis andStochastics (WIAS) (joint work with Michael Hintermüller,
Carlos Rautenberg)
Directional differentiability of quasi-variational inequalities
and related optimization problems
In this talk, which is based on recent work with Prof. Michael
Hintermüller and Dr. Carlos N. Rautenberg, I will discuss the
directional differentiability of solution maps associated to elliptic quasi-variational inequalities (QVIs) of obstacle type. QVIs
are generalisations of variational inequalities (VIs) where theconstraint set associated to the inequality also depends on
the solution, adding an extra source of potential nonlinearity
and nonsmoothness to the problem. I will show that the map
taking the source term into the set of solutions is directionallydifferentiable in a certain sense and give an outline of theproof, and we will see that the directional derivative can becharacterised as the monotone limit of directional derivatives
associated to certain VIs. If time allows, the theory will beillustrated with an application of QVIs in thermoforming.
Yuxin Chen, Princeton University (joint work with EmmanuelJ. Candes)
Solving Random Quadratic Systems of Equations Is Nearlyas Easy as Solving Linear Systems
We consider the fundamental problem of solving quadraticsystems of equations in n variables. We propose a novel method,which starting with an initial guess computed by means of aspectral method, proceeds by minimizing a nonconvex functional. There are several key distinguishing features, mostnotably, a distinct objective functional and novel update rules,
which operate in an adaptive fashion and drop terms bearing too much influence on the search direction. These carefulselection rules provide a tighter initial guess, better descentdirections, and thus enhanced practical performance. On thetheoretical side, we prove that for certain unstructured modelsof quadratic systems, our algorithms return the correct solution
in linear time, i.e. in time proportional to reading the data
as soon as the ratio between the number of equations andunknowns exceeds a fixed numerical constant. We extend thetheory to deal with noisy systems and prove that our algorithms
achieve a statistical accuracy, which is nearly un-improvable.
We complement our theoretical study with numerical examples showing that solving random quadratic systems is bothcomputationally and statistically not much harder than solving
linear systems of the same size. For instance, we demonstrate

14

Special Events

empirically that the computational cost of our algorithm isabout four times that of solving a least-squares problem of thesame size.
Damek Davis, Cornell University (joint work with Dmitriy
Drusvyatskiy, Sham Kakade, Jason D. Lee)
Stochastic Subgradient Method Converges on Tame Functions
The stochastic subgradient method forms the algorithmiccore of modern statistical and machine learning. We understandmuch about how it behaves for problems that are smoothor convex, but what guarantees does it have in the absenceof smoothness and convexity? We prove that the stochastic
subgradient method, on any semialgebraic locally Lipschitz
function, produces limit points that are all first-order stationary.
More generally, our result applies to any function with a Whitney
stratifiable graph. In particular, this work endows the stochastic
subgradient method, and its proximal extension, with rigorous
convergence guarantees for a wide class of problems arising indata science—including all popular deep learning architectures.

Xudong Li, Fudan University
Exploiting Second Order Sparsity in Big Data Optimization
In this talk, we shall demonstrate how second order sparsity(SOS) in important optimization problems can be exploited
to design highly efficient algorithms. The SOS property appears naturally when one applies a semismooth Newton (SSN)
method to solve the subproblems in an augmented Lagrangian
method (ALM) designed for certain classes of structured convex
optimization problems. With in-depth analysis of the underlying
generalized Jacobians and sophisticated numerical implementation, one can solve the subproblems at surprisingly low costs.
For lasso problems with sparse solutions, the cost of solving asingle ALM subproblem by our second order method is comparable or even lower than that in a single iteration of manyfirst order methods. Consequently, with the fast convergenceof the SSN based ALM, we are able to solve many challenging
large scale convex optimization problems in big data applications efficiently and robustly. For the purpose of illustration, we
present a highly efficient software called SuiteLasso for solving
various well-known Lasso-type problems.

Plenary Talk
Tue 09:00–10:00 H 0105
Uday Shanbhag, Penn State University
Addressing Uncertainty in Equilibrium Problems
Chair: Jong-Shi Pang
We address two sets of fundamental
problems in the context of equilibrium
problems: (i) Of these, the first pertains towards the development of existence statements for stochastic variational inequality and complementarity
problems. A naive application of traditional deterministic existence theory
requires access to closed-form expressions of the expectation-valued map;
instead, we develop a framework underwhich verifiable a.s. requirements on the map allow for making
existence claims in a broad set of monotone and non-monotone
regimes. Time permitting, we consider how such avenues canallow for building a sensitivity theory for stochastic variational
inequality problems. (ii) Our second question considers thedevelopment of synchronous, asynchronous, and randomized inexact best-response schemes for stochastic Nash games where
an inexact solution is computed by using a stochastic gradientmethod. Under a suitable spectral property on the proximal
best-response map, we show that the sequence of iterates
converges to the unique Nash equilibrium at a linear rate. In
addition, the overall iteration complexity (in gradient steps) isderived and the impact of delay, asynchronicity, and randomization is quantified. We subsequently extend these avenues
to address the distributed computation of Nash equilibria overgraphs in stochastic regimes where similar rate statements
can be derived under increasing rounds of communication andvariance reduction.

Special Events

Memorial Session
Tue.4 16:30–17:45 H 0105
Reflections on the Life and Work of Andrew R. Conn
Chair: Michael Overton
Andrew R. Conn passed away on
March 14, 2019. He played a pioneering role in nonlinear optimization
and remained very active in the field
until shortly before his death. At
this session, 5 of his friends andcolleagues will give 10-minute tributes
to his work and his life. These will
be followed by 30 minutes where
everyone will have the opportunity tocontribute a few words in memory ofAndy, as he was always known. The five speakers are:
Philippe L. Toint, University of Namur
Katya Scheinberg, Lehigh University
Annick Sartenaer, University of Namur
Henry Wolkowicz, University of Waterloo
James Burke, University of Washington
Andrew R. Conn was born in London on Aug. 6, 1946, anddied at his home in Westchester County, NY, on Mar. 14, 2019.
He played a pioneering role in nonlinear optimization startingin the early 1970s, and he remained very active in the field
until shortly before his death. Andy obtained his B.Sc. withhonours in Mathematics at Imperial College, London, in 1967,
and his Ph.D. from the University of Waterloo in 1971. He wasa faculty member at Waterloo for 18 years starting in 1972,
and then moved to the IBM T.J. Watson Research Center in
Yorktown Heights, NY, where he spent the rest of his career.
Andy’s research interests were extensive but centered on
nonlinear optimization. They were typically motivated by algorithms, and included convergence analysis, applications andsoftware. His first published paper, Constrained optimization
using a nondifferentiable penalty function, which appeared in
SIAM J. Numer. Anal. in 1973, was already quite influential,
helping to pave the way for the eventually widespread use ofexact penalty functions in nonlinear programming. Later work
included an extraordinarily productive collaboration with Nick
Gould and Philippe Toint, resulting in more than 20 publications by these three authors alone. One highlight of thiscollaboration was the definitive treatise Trust Region Methods(2000), the first book in the optimization series published by the
Mathematical Programming Society (MPS, later MOS) in collaboration with SIAM. Another highlight was the LANCELOT
software for large-scale constrained optimization, for which
the authors were awarded the prestigious Beale–Orchard-Hays
Prize for Excellence in Computational Mathematical Programming in 1994. A third highlight was the introduction of theinfluential Constrained and Unconstrained Testing Environment (CUTE). In more recent years, Andy became interested
in derivative-free optimization (DFO) and, together with Katya
Scheinberg and Luis Nunes Vicente, published several papers
on DFO as well as a book, Introduction to Derivative-Free
Optimization (MPS-SIAM Series on Optmization, 2009), forwhich they were awarded the Lagrange Prize in Continuous
Optimization by MOS and SIAM in 2015. The citation statesthat the book “includes a groundbreaking trust region framework for convergence that has made DFO both principled andpractical”.
Andy is survived by his wife Barbara, his mother and four
siblings in England, his daughter Leah and his son Jeremy, andthree grandchildren. He will be greatly missed by his family,
the optimization community, and his many friends.

15

Plenary Talk
Wed 09:00–10:00 H 0105
Tamara G. Kolda, Sandia National Laboratories
Randomized Methods for Low-Rank Tensor Decomposition
Chair: Kim-Chuan Toh
Tensor decomposition discovers latent structure in higher-order data sets
and is the higher-order analogue of thematrix decomposition. Variations haveproliferated, including symmetric tensor decomposition, tensor completion,higher-order singular value decomposition, generalized canonical tensor decomposition, streaming tensor decomposition, etc. All of these scenarios reduce to some form of nonconvex optimization. In this talk, we discuss the use of randomized
optimization methods, including stochastic gradient methods
and sketching. Such approaches show amazing promise, notonly improving the speed of the methods but also the qualityof the solution. We give examples of the intriguing successes aswell as notable limitations of these methods. We close with anumber of open questions, including important open theoreticalchallenges.

16Special Events

Semi-Plenary Talk

Semi-Plenary Talk

Wed 10:15–11:00 H 0105

Wed 10:15–11:00 H 0104

Frauke Liers, Friedrich-Alexander-Universität ErlangenNürnberg
Robust Optimization for Energy Applications: Current
Challenges and Opportunities

Radu Boţ, University of Vienna
Proximal Algorithms for Nonsmooth Nonconvex Optimization

Chair: Wolfram Wiesemann
In this talk, we will give an overview
over optimization problems under uncertainty as they appear in energy management, together with global solution
approaches. One way of protecting
against uncertainties that occur in realworld applications is to apply and todevelop methodologies from robust optimization. The latter takes these uncertainties into account already in themathematical model. The task thenis to determine solutions that are feasible for all considered
realizations of the uncertain parameters, and among them
one with best guaranteed solution value. We will introduce
a number of electricity and gas network optimization problems for which robust protection is appropriate. Already forsimplified cases, the design of algorithmically tractable robust
counterparts and global solution algorithms is challenging. As
an example, the stationary case in gas network operations iscomplex due to non-convex dependencies in the physical statevariables pressures and flows. In the robust version, two-stage
robust problems with a non-convex second stage are to besolved, and new solution methodologies need to be developed.
We will highlight robust optimization approaches and conclude
by pointing out some challenges and generalizations in thefield.

Chair: Russell Luke
During the last decade many efforts
have been made to translate the powerful proximal algorithms for convex
optimization problems to the solvingof nonconvex optimization problems.
In this talk we will review such iterative
schemes which posses provable convergence properties and are designed tosolve nonsmooth nonconvex optimization problems with structures whichrange from simple to complex. We will
discuss the pillars of the convergence analysis, like the boundedness of the generated iterates, techniques to prove that cluster
points of this sequence are stationary points of the optimization problem under investigation, and elements of nonconvex
subdifferential calculus. We will also emphasize the role playedby the Kurdyka-Lojasiewicz property when proving global convergence, and when deriving convergence rates in terms of the
Lojasiewicz exponent.

Special Events

Semi-Plenary Talk

17

Semi-Plenary Talk

Wed 17:30–18:15 H 0105

Wed 17:30–18:15 H 0104

Heinz Bauschke, University of British Columbia
On the Douglas-Rachford algorithm

Lieven Vandenberghe, UCLA
Structured sparsity in semidefinite programming

Chair: Jonathan Eckstein

Chair: Jacek Gondzio

The Douglas-Rachford algorithm isa popular method for finding minimizers of sums of nonsmooth convex functions; or, more generally, zeros of sumsof maximally monotone operators.
In this talk, I will focus on classicaland recent results on this method aswell as challenges for the future.

Primal-dual interior-point methods
for semidefinite programming, as implemented in widely used solvers, relyon the elegant theory of self-scaled barriers and the property that the dense
symmetric positive semidefinite matrices form a symmetric convex cone.
In applications it is very common
that the constraints in a semidefinite
program are sparse. It is natural topose such problems as conic optimization problems over cones of sparse positive semidefinite matrices, in order to exploit theory and algorithms from sparse linear
algebra. Examples of this approach are the splitting techniques
based on decomposition theorems for chordal sparsity patterns,
which have been applied successfully in first-order methods andinterior-point algorithms. However sparse semidefinite matrix
cones are not symmetric, and conic optimization algorithms forthese cones lack the extensive theory behind the primal-dual
methods for dense semidefinite optimization. The talk will
explore new formulations of primal-dual interior-point methods
for sparse semidefinite programming. It will mostly focus on
the important special case of chordal sparsity patterns thatdefine homogeneous convex cones.
The talk is based on joint work with Levent Tuncel.

18

Special Events

Semi-Plenary Talk

Semi-Plenary Talk

Thu 15:15–16:00 H 0105

Thu 15:15–16:00 H 0104

Frank E. Curtis, Lehigh University
New Quasi-Newton Ideas for (Non)smooth Optimization

Piermarco Cannarsa, University of Rome Tor Vergata
First order mean field games

Chair: Jorge Nocedal

Chair: Stefan Ulbrich

One of the most important developments in the design of optimization
algorithms came with the advent ofquasi-Newton methods in the 1960s.
These methods have been used togreat effect over the past decades, andtheir heyday is not yet over. In thistalk, we introduce a new advance inquasi-Newton methodology that werefer to as displacement aggregation.
We also discuss other new advancesfor extending quasi-Newton ideas for stochastic and nonconvex,
nonsmooth optimization.

The theory of mean field games has
been developed in the last decade byeconomists, engineers, and mathematicians in order to study decision making in very large populations of smallinteracting agents. The approach by
Lasry and Lions leads to a system ofnonlinear partial differential equations,the solution of which can be used toapproximate the limit of an N-player
Nash equilibrium as N tends to infinity.
This talk will mainly focus on deterministic models, which areassociated with a first order pde system. The main points thatwill be addressed are the existence and uniqueness of solutions,
their regularity in the presence of boundary conditions, andtheir asymptotic behaviour as time goes to infinity.

Special Events

Plenary Talk
Thu 16:15–17:15 H 0105
Michael Ulbrich, Technical University of Munich
Nonsmoothness in PDE-Constrained Optimization
Chair: Matthias Heinkenschloss
Nonsmoothness arises in PDEconstrained optimization in multifaceted ways and is at the core ofmany recent developments in this field.
This talk highlights selected situations
where nonsmoothness occurs, either
directly or by reformulation, and discusses how it can be handled analytically and numerically.
Projections, proximal maps, and similar techniques provide powerful tools
to convert variational inequalities and stationarity conditions
into nonsmooth operator equations. These then enable theapplication of semismooth Newton methods, which belong
to the most successful algorithms for inequality constrained
problems with PDEs. Nonsmoothness also arises in problems
with equilibrium constraints, such as MPECs, or with bilevelstructure. This is particularly apparent when reduced problems
are generated via solution operators of subsystems. Similarly,
nonlinear PDEs, even if looking smooth at first sight, often
pose challenges regarding the differentiability of the solution
operator with respect to parameters such as control or shape.
There are also important applications where nonsmoothness
arises naturally in the cost function, for instance in sparsecontrol or when risk measures like the CVaR are used in riskaverse PDE-constrained optimization. Also other approaches
for PDE-constrained optimization under uncertainty exhibit
nonsmoothness, e.g., (distributionally) robust optimization.
All this shows that PDE-constrained optimization with nonsmooth structures is a broad and highly relevant field. This talkdiscusses specific examples to highlight the importance of being
able to master nonsmoothness in PDE optimization. General
patterns are identified and parallels to the finite dimensionalcase are explored. The infinite dimensional setting of PDEs,
however, poses additional challenges and unveils problem characteristics that are still present after discretization but thenare much less accessible to a rigorous analysis.

19

20

Exhibition

Exhibition

FBP 2020

The exhibition is open during the full conference period.

Springer Nature Switzerland AG – Birkhäuser
Address:
Picassoplatz 4
City:
Basel
Zip:
4052
Country:
Switzerland
Contact person: Clemens Heine
Email address:
clemens.heine@birkhauser-science.com
URL:
www.springer.com

2020
International Conference on
Free Boundary Problems:
Theory and Applications

SIAM – Society for Industrial and Applied Mathematics
Address:
3600 Market Street – 6th Floor
City:
Philadelphia, PA
Zip:
19104
Country:
USA
Contact person: Michelle Montgomery
Email address:
montgomery@siam.org
URL:
www.siam.org

The 15th International Conference on Free Boundary
Problems: Theory and Applications 2020 (FBP 2020)
will take place on the campus of the Humboldt University (HU) of Berlin, September 7–11, 2020. The FBP
conference is a flagship event that brings together thefree boundary/partial differential equation community
and is organized every few years.
The Call For Papers is expected to be sent in Fall 2019.
More information on https://fbp2020.dePosters Overview

21

Posters Overview
Waleed Alhaddad
Uncertainty Quantification in Wind Power Forecasting

1

Anas Barakat
2
Convergence of the ADAM algorithm from a Dynamical
System Viewpoint
Jo Andrea Brüggemann
3
Elliptic obstacle-type quasi-variational inequalities (QVIs)
with volume constraints motivated by a contact problem inbiomedicine

Adrian Redder
20
Learning based optimal control and resource allocation forlarge-scale cyber-physical systems.
Olaf Schenk
Balanced Graph Partition Refinement in the p-Norm

21

Johanna Schultes
22
Biobjective Shape Optimization of Ceramic Components using
Weighted Sums: Reliability versus Cost

4

Roya Soltani [canceled]23
Robust optimization for the redundancy allocation problem
with choice of component type and price discounting

Jongchen Chen
5
Towards Perpetual Learning Using a Snake-Like Robot
Controlled by a Self-Organizing Neuromolecular System

Alexander Spivak
24
Successive Approximations for Optimal Control in Nonlinear
Systems with Small Parameter

Sarah Chenche
A new approach for solving the set covering problem

Steven-Marian Stengl
25
On the Convexity of Optimal Control Problems involving
Nonlinear PDEs or VIs

Renzo Caballero
Stochastic Optimal Control of Renewable Energy

6

Maxim Demenkov
7
First-order linear programming over box-constrained polytopes
using separation oracle
Simon Funke
8
Algorithmic differentation for coupled FEniCS and pyTorchmodelsCarmen GräßleTime adaptivity in POD based model predictive control

Meihua Wang
27
Application of quasi-norm regularization into robust portfolio
optimization problem

9

Seyedeh Masoumeh Hosseini Nejad
10
Accelerated Bregman proximal gradient methods for theabsolute value equation
Tomoyuki Iori
11
A Symbolic-Numeric Penalty Function Method for Parametric
Polynomial Optimizations via Limit Operation in a Projective
Space
Chesoong Kim
12Modeling and Optimization for Wireless Communication
Networks using Advanced Queueing Model
Elisabeth Köbis
13
An Inequality Approach to Set Relations by Means of a
Nonlinear Scalarization Functional
Markus Arthur Köbis
A New Set Relation in Set Optimization

Adrian Viorel
26
An average vector field approach to second-order nonconvex
gradient systems

14

Chul-Hee Lee
15
Optimization and Experimental Validation on the Frame of
Resistance Spot Welding Gun
Chiao-Wei Li
16
Data-driven equilibrium pricing in a multi-leader-one-follower
game
Sebastian Lämmel
On Comparison of Experiments due to Shannon

17

Martin Morin
Stochastic Variance Adjusted Gradient Descent

18

Ketan Rajawat
19
Nonstationary Nonparametric Optimization: Online Kernel
Learning against Dynamic Comparators

Yue Xie28
Resolution of nonconvex optimization via ADMM type ofschemesPeng-Yeng Yin
29
Simulation Optimization of Co-Sited Wind-Solar Farm underWind Irradiance and Dust Uncertainties
Jia-Jie Zhu
30
A Learning Approach to Warm-Start Mixed-Integer Optimization for Controlling Hybrid Systems

22

Posters Abstracts

Posters Abstracts
1 Waleed Alhaddad, King Abdullah University of Scienceand Technology (KAUST)
Uncertainty Quantification in Wind Power Forecasting
Reliable wind power generation forecasting is crucial to meetenergy demand, to trade and invest. We propose a model tosimulate and quantify uncertainties in such forecasts. Thismodel is based on Stochastic Differential Equations whose
time-dependent parameters are inferred using continuous optimization of an approximate Likelihood function. The result isa skew stochastic process that simulates uncertainty of wind
power forecasts accounting for maximum power production
limit and other temporal effects. We apply the model to historical Uruguayan data and forecasts.
2 Anas Barakat, LTCI, Télécom Paris, Institut Polytechnique de Paris (joint work with Pascal Bianchi)
Convergence of the ADAM algorithm from a Dynamical
System Viewpoint
Adam is a popular variant of the stochastic gradient descent.
However, only few information about its convergence is available in the literature. We investigate the dynamical behavior of
Adam when the objective function is non-convex and differentiable. We introduce a continuous-time version of Adam, under
the form of a non-autonomous ordinary differential equation.
Existence, uniqueness and convergence of the solution to thestationary points of the objective function are established. It
is also proved that the continuous-time system is a relevant
approximation of the Adam iterates.
3
Jo Andrea Brüggemann, Weierstrass Institute for Applied Analysis and Stochastics (WIAS)
Elliptic obstacle-type quasi-variational inequalities (QVIs)
with volume constraints motivated by a contact problem
in biomedicine
Motivated by a contact model in biomedicine, a compliant
obstacle problem arises exemplarily in the situation of two elastic membranes enclosing a region of constant volume, which
are subject to external forces. The motivating model enables
a reformulation as a obstacle-type quasi-variational inequality (QVI) with volume constraints. We analyse this class of
QVIs and present a tailored path-following semismooth Newton
method to solve the problem. In view of the numerical performance, adaptive finite elements are used based on a posteriori
error estimation.
4
Renzo Caballero, King Abdullah University of scienceand technology (KAUST) (joint work with Raul Tempone)
Stochastic Optimal Control of Renewable Energy
Uruguay has always been a pioneer in the use of renewablesources of energy. Nowadays, it can usually satisfy its total
demand from renewable sources, but half of the installed power,
due to wind and solar sources, is non-controllable and has highuncertainty and variability. We deal with non-Markovian dynamics through a Lagrangian relaxation, solving then a sequence of
HJB PDEs associated with the system to find time-continuous
optimal control and cost function. We introduce a monotone
scheme to avoid spurious oscillations. Finally, we study theusefulness of extra system storage capacity.
5 Jongchen Chen, National Yunlin University of Scienceand Technology (joint work with Chao-Yi Huang)
Towards Perpetual Learning Using a Snake-Like Robot
Controlled by a Self-Organizing Neuromolecular System
A robot that works no matter how well designed, the resultsof its operation in the actual environment are sometimes quite
different from the expected results of its design environment.
In this study, we train a snake-type robot Snaky with 8 joints

and 4 control rotary motors to learn to complete the specified
snake mission through an autonomous learning mechanism.
The core learning mechanism is the artificial neuromolecularsystem (ANM) developed by the research team in the early days.
The learning continues until the robot achieves the assigned
tasks or stopped by the system developer.
6 Sarah Chenche, Guelma University Algeria
A new approach for solving the set covering problem
The paper presents a simple technique to heuristically reducethe column set of set covering problems. The idea consists indiscarding all columns that are not selected by the subgradientalgorithm used to solve the Lagrangian relaxation. In case thereduction leaves a large number of columns, this filter may bestrengthened by discarding all columns that are not selected
more than alpha% times.the proposed technique is in the classof “Sifting” techniques. We are evaluating this technique using
a Benders algorithm designed for solving real world set covering
instances arising from Deutsche Bahn.
7 Maxim Demenkov, Institute of Control Sciences
First-order linear programming over box-constrained polytopes using separation oracle
First-order methods for linear feasibility problems have been
known since the Kaczmarz algorithm. According to Naumann
and Klee, any polytope is obtainable as a section of higherdimensional cube. Transformation of linear constraints to suchform is very much like the one in simplex method, but insteadof non-negativity constraints every variable is constrained to
an interval. A solution of LP problem amounts to finding anintersection between a line and an affine transformation of
the cube, which iteratively uses calculation of a hyperplane
separating a point on the line and that polytope.
8 Simon Funke, Simula Research Laboratory (joint workwith Sebastian Mitusch)
Algorithmic differentation for coupled FEniCS and pyTorchmodelsThis poster presents recent advances in dolfin-adjoint, thealgorithmic differentiation tool for the finite element framework
FEniCS. We will show dolfin-adjoint can be used to differentiated and optimised PDE models in FEniCS that are coupled
to neural network models implemented in pyTorch. Our approach allows to compute derivatives with respect to weights
in the neural network, and as PDE coefficients such as initialcondition, boundary conditions and even the mesh shape.
9 Carmen Gräßle, Universität Hamburg (joint work withMichael Hinze, Alessandro Alla)
Time adaptivity in POD based model predictive control
Within model predictive control (MPC), an infinite time
horizon problem is split into a sequence of open-loop finite
horizon problems which allows to react to changes in the
problem setting due to external influences. In order to findsuitable time horizon lengths, we utilize a residual-based time
adaptive approach which uses a reformulation of the optimality
system of the open-loop problem as a biharmonic equation. In
order to achieve an efficient numerical solution, we apply POD
based model order reduction.
10 Seyedeh Masoumeh Hosseini Nejad (joint work with Majid Darehmiraki)
Accelerated Bregman proximal gradient methods for theabsolute value equation
The absolute value equation plays a outstanding role in optimization. In this paper, we employ proximal gradient method
to present a efficient solution for solving it. We use accelerated

Posters Abstracts

Bregman proximal gradient methods that employ the Bregman
distance of the reference function as the proximity measure.
11
Tomoyuki Iori, Kyoto University (joint work with
Toshiyuki Ohtsuka)
A Symbolic-Numeric Penalty Function Method for Parametric Polynomial Optimizations via Limit Operation in a
Projective Space
We propose a symbolic-numeric method to solve a constrained optimization problem with free parameters where all
the functions are polynomials. The proposed method is basedon the quadratic penalty function method and symbolically
performs the limit operation of a penalty parameter by using
projective spaces and tangent cones. As its output, we obtainan implicit function representation of optimizers as a functionof the free parameters. Some examples show that the exact
limit operation enables us to find solutions that do not satisfy
the Karush-Kuhn-Tucker (KKT) conditions.
12 Chesoong Kim, Sangji University
Modeling and Optimization for Wireless Communication
Networks using Advanced Queueing Model
We consider an advanced queueing model with energy harvesting and multi-threshold control by the service modes. The
account of the possibility of error occurrence is very importantin modelling wireless sensor networks. This model generalizes
a previously considered in the literature by suggestions thatthe buffer for customers has an infinite capacity and the accumulated energy can leak from the storage. Under the fixed
thresholds defining the control strategy, behavior of the system
is described by multi-dimensional Markov chain what allows toformulate and solve optimization problems.
13
Elisabeth Köbis, Martin Luther University HalleWittenberg (joint work with Markus Arthur Köbis)
An Inequality Approach to Set Relations by Means of a
Nonlinear Scalarization Functional
We show how a nonlinear scalarization functional can be usedin order to characterize several well-known set relations andwhich thus plays a key role in set optimization. By means of thisfunctional, we derive characterizations for minimal elementsof set-valued optimization problems using a set approach. Our
methods do not rely on any convexity assumptions on theconsidered sets. Furthermore, we introduce a derivative-freedescent method for set optimization problems without convexity
assumptions to verify the usefulness of our results.
14 Markus Arthur Köbis, Free University Berlin (joint workwith Jia-Wei Chen, Elisabeth Köbis, Jen-Chih Yao)
A New Set Relation in Set Optimization
We show the construction of a novel set relation implementing a compromise between the known concepts “upper set lessrelation” on the one hand and “lower set less relation” on theother. The basis of the construction lies in the characterizationof the relation of two sets by means of a single real valuethat can be computed using nonlinear separation functionals of
Tammer-Weidner-type. The new relation can be used for thedefinition of flexible robustness concepts and the theoretical
study of set optimization problems based on this concepts canbe based on properties of the other relations.
15 Chul-Hee Lee, INHA University (joint work with Ji-Su
Hong, Gwang-Hee Lee)
Optimization and Experimental Validation on the Frameof Resistance Spot Welding Gun
Resistance spot welding gun is generally used to bond partsin the automotive and home appliance industry. The industrial

23

robot is essential because most of the welding process is automated. The weight of the welding gun should be minimized
to increase the efficiency of the robot motion. In this study,
welding gun frame optimization is performed using genetic algorithm methods. Several parts of frame thickness are specified
as variables and the optimization model is validated throughboth simulation and experiments. The weight is decreased by
up to 14% and cost saving is also achieved.
16 Chiao-Wei Li, National Tsing Hua University (joint workwith Yong-Ge Yang, Yu-Ching Lee, Po-An Chen, Wen-Ying
Huang)
Data-driven equilibrium pricing in a multi-leader-onefollower game
We consider a periodic-review pricing problem. There arefirms i=1,...,N competing in the market of a commodity in astationary demand environment. Each firm can modify the priceover discretized time. The firm i’s demand at period n consistsof the linear model conditional on the selling prices of its own
and other firms with an independent and identically distributed
random noise. The coefficients of the linear demand model is
not known a priori. Each firms’ objective is to sequentially set
prices to maximize revenues under demand uncertainty andcompetition.
17 Sebastian Lämmel, Technische Universität Chemnitz(joint work with Vladimir Shikhman)
On Comparison of Experiments due to Shannon
In order to efficiently design experiments, decision makers
have to compare them. The comparison of experiments due
to Blackwell is based on the notion of garbling. It is wellknown that the Blackwell order can be characterized by agents’maximal expected utility. We focus on the comparison of experiments due to Shannon by allowing pre-garbling. We showthat for any subset of decision makers the comparison of theirmaximal expected utilities does not capture the Shannon order. For characterizing the Shannon order, we enable decision
makers to redistribute noise, and convexify the garbling.
18 Martin Morin, Lund University (joint work with Pontus
Giselsson)
Stochastic Variance Adjusted Gradient Descent
We present SVAG, a variance reduced stochastic gradientmethod with SAG and SAGA as special cases. A parameter interpolates and extrapolates SAG/SAGA such that the variancethe gradient approximation can be continuously adjusted, at thecost of introducing bias. The intuition behind this bias/variance
trade-off is explored with numerical examples. The algorithm
is analyzed under smoothness and convexity assumptions andconvergence is established for all choices of the bias/variance
trade-off parameter. This is the first result that simultaneously
captures SAG and SAGA.
19 Ketan Rajawat, Indian Institute of Technology Kanpur(joint work with Amrit Singh Bedi, Alec Koppel)
Nonstationary Nonparametric Optimization: Online Kernel Learning against Dynamic Comparators
We are interested in solving an online learning problem
when the decision variable is a function f which belongs to
reproducing kernel Hilbert space. We present the dynamic
regret analysis for an optimally compressed online function
learning algorithm. The dynamic regret is bounded in termsof the loss function variation. We also present the dynamic
regret analysis in terms of the function space path length. We
prove that the learned function via the proposed algorithm
has a finite memory requirement. The theoretical results arecorroborated through simulation on real data sets.

24

Posters Abstracts

20 Adrian Redder, Automatic Control Group / Paderborn
University (joint work with Arunselvan Ramaswamy, Daniel E.
Quevedo)Learning based optimal control and resource allocation forlarge-scale cyber-physical systems.
Typical cyber-physical Systems consist of many interconnected heterogeneous entities that share resources such ascommunication or computation. Here optimal control and resource scheduling go hand in hand. In this work, we consider the
problem of simultaneous control and scheduling. Specically, we
present DIRA, a Deep Reinforcement Learning based Iterative
Resource Allocation algorithm, which is scalable and control
sensitive. For the controller, we present three compatible designs based on actor-critic reinforcement learning, adaptive
linear quadratic regulation and model predictive control.
21 Olaf Schenk, Università della Svizzera italiana (joint
work with Dimosthenis Pasadakis, Drosos Kourounis)
Balanced Graph Partition Refinement in the p-Norm
We present a modified version of spectral partitioning utilizing the graph p-Laplacian, a nonlinear variant of the standard
graph Laplacian. The computation of the second eigenvector
is achieved with the minimization of the graph p-Laplacian
Rayleigh quotient, by means of an accelerated gradient descentapproach. A continuation approach reduces the p-norm froma 2-norm towards a 1-norm, thus promoting sparser solution
vectors, corresponding to smaller edgecuts. The effectivenessof the method is validated in a variety of complex graphicalstructures originating from power and social networks.
22 Johanna Schultes, University of Wuppertal (joint workwith Kathrin Klamroth, Michael Stiglmayr, Onur Tanil Doganay,
Hanno Gottschalk, Camilla Hahn)
Biobjective Shape Optimization of Ceramic Components
using Weighted Sums: Reliability versus Cost
We consider a biobjective PDE constrained shape optimization problem maximizing the reliability of a ceramic component
under tensile load and minimizing its material consumption.
The numerical implementation is based on a finite element
discretization with a B-spline representation of the shapes. Using the B-spline coefficients as control variables we combine itwith a single objective gradient descent algorithm for solving
the weighted sum scalarization and apply it to 2D test cases.
While varying the weights we determine different solutions
representing different preferences.
23 [canceled] Roya Soltani, Khatam University (joint workwith Reza Soltani, Soheila Soltani)
Robust optimization for the redundancy allocation problem with choice of component type and price discounting
In this paper, a redundancy allocation problem (RAP) withan active strategy and choice of a component type is considered,in which there are different components with various reliabilities
and costs considered as imprecise and varied in an interval
uncertainty set. Therefore, a robust model for the RAP ispresented to propose a robust design for different realization of
uncertain parameters. Finally, uncertainty and price discounting
are considered together in a comprehensive model for the RAP
and the effects on warranty costs with minimal repair are
investigated.
24 Alexander Spivak, HIT - Holon Institute of Technology
Successive Approximations for Optimal Control in Nonlinear Systems with Small Parameter
Bilinear systems are linear on phase coordinates when thecontrol is fixed, and linear on the control when the coordinates are fixed. They describe the dynamic processes of nuclear

reactors, kinetics of neutrons, and heat transfer. Further investigations show that many processes in engineering, biology,
ecology and other areas can be described by the bilinear systems . The problem is to find optimal control minimizing thequadratic functional for the system driven by white noise withsmall parameter. Successive approximations are constructed byperturbation theory method/
25 Steven-Marian Stengl, WIAS/HU Berlin
On the Convexity of Optimal Control Problems involving
Nonlinear PDEs or VIs
In recent years, generalized Nash equilibrium problems infunction spaces involving control of PDEs have gained increasing interest. One of the central issues arising is the questionof existence, which requires the topological characterization of
the set of minimizers for each player. In this talk, we propose
conditions on the operator and the functional, that guarantee
the reduced formulation to be a convex minimization problem.
Subsequently, we generalize the results of convex analysis toderive optimality systems also for nonsmooth operators. Ourfindings are illustrated with examples.
26 Adrian Viorel, Tehnical University of Cluj-Napoca
An average vector field approach to second-order nonconvex gradient systems
The average vector field (AVF) discrete gradient, originallydesigned by Quispel and McLaren as an energy-preserving
numerical integrator for Hamiltonian systems has found a variety of applications, including Optimization. In contrast to
the very recent work of Ehrhardt et. al. which deals with AVF
discretizations of gradient flows, the present contribution aims
at deriving discrete versions of second-order gradient systems
based on AVF discrete gradients. The obtained optimization
algorithms are shown to inherit the asymptotic behavior of
their continuous counterparts.
27
Meihua Wang, Xidian University (joint work with
Caoyuan Ma, Fengmin Xu)
Application of quasi-norm regularization into robust portfolio optimization problem
This paper concerns robust portfolio optimization problem
which is a popular topic in financial engineering. To reach theinvestor’s requirement on asset selection, we build a sparserobust portfolio optimization model by adding a nonconvex
penalty item into the objective function instead of the cardinality constraint. A series of models based on different uncertainty
sets for expected return rates and covariance are present. The
Alternating Quadratic Penalty (AQP) method is applied tosolve the relative sparse robust portfolio selection models.
28 Yue Xie, University of Wisconsin-Madison (joint workwith Uday Shanbhag, Stephen Wright)
Resolution of nonconvex optimization via ADMM type ofschemes
First, we consider a class of nonconvex optimization problems
including l0 norm minimization and dictionary learning which
can be efficiently solved using ADMM and its variants. In particular, the nonconvex problem is decomposed such that each
subproblem is either a tractable convex program or endowedwith hidden convexity. Competitive performance is demonstrated through preliminary numerics. Second, we show thatfor a general class of nonconvex optimization problems, proximal ADMM is able to avoid strict saddle points with probability
1 using random initialization.

Posters Abstracts

29 Peng-Yeng Yin, National Chi Nan University (joint workwith Chun-Ying Cheng)
Simulation Optimization of Co-Sited Wind-Solar Farm under Wind Irradiance and Dust Uncertainties
This paper considers a hybrid system deploying both windand solar photovoltaic (PV) farms at the same site. We propose
a simulation optimization approach. The simulation of uncertain wind, sky irradiance, and dust deposition is based on ameteorological dataset. The co-siting constraints which degrade
the production are handled by calculating wind-turbine’s umbra
and penumbra on PVs, planning shortest roads for routing themaintenance vehicles, and minimizing the land usage conflictby an evolutionary algorithm. We propose several models toleverage the production, cost, and uncertainty.
30

Jia-Jie Zhu, Max Planck Institute for Intelligent Systems

A Learning Approach to Warm-Start Mixed-Integer Optimization for Controlling Hybrid Systems
Motivated by practical challenges in controlling hybrid systems (i.e. robot trajectory optimization through contact), we
propose a machine learning approach to speed up the mixedinteger optimization (MIO) for numerical optimal control. We
learn policies to serve as a form of solution memory by casting the training as learning from suboptimal demonstrations.
The resulting policies provide feasible incumbent solutions forthe branch-and-bound (-cut) process of MIO. Coupled withother insights developed in this work, the approach speeds up
numerical optimal control of hybrid systems.

25

26

Conference Dinner

Conference Dinner: Cruise along the river Spree on the “MS Mark Brandenburg”

The river cruise starts at Gotzkowskybrücke, a bridge in 20 minutes walking distance from the conference venue. You canalternatively take Bus 245 from Ernst-Reuter-Platz to Franklinstr., see maps on page 6, and on the inside-back cover. We willthen cruise through the city of Berlin while having dinner from buffet with modern German food.

Important Facts
Time and Date Tuesday, August 6, 19:00–23:00
Venue River Cruise on “MS Mark Brandenburg”, Stern und Kreisschifffahrt GmbH.
Start Gotzkowskybrücke, 10555 Berlin, Stern und Kreisschifffahrt pier
Boarding starts at 18:30, check-in no later than 19:00.
End 23:00 at the starting point
Participation fee 65 EUR
Tickets The Conference Dinner is open to attendees and guests who registered and paid in advance for tickets. Tickets areincluded in your registration envelope. There may be a limited number of tickets available on site — please enquire at the
ICCOPT Registration Desk.
Menu Vegetarian options will be available. Please advise, in advance, of any dietary requirements via email to dinner@iccopt2019.berlin
Contact dinner@iccopt2019.berlin
Please note that we assume no liability for damage to property or personal injury.

Parallel Sessions Overview

Parallel Sessions Overview

Mon.1 11:00–12:15
Mon.1 H 0107 Optimization in Healthcare (Part I), Felix Jost, Christina Schenk
Marta Sauter

Leonard Wirsching

Christina Schenk

page 39

Mon.1 H 0104 Optimization for Data Science (Part I), Ying Cui, Jong-Shi Pang
Jong-Shi Pang

Defeng Sun

Gesualdo Scutari

page 39

Mon.1 H 3004 Non-Convex Optimization for Neural Networks, Mingyi Hong, Ruoyu Sun
Ruoyu Sun

Sijia Liu

Alex Schwing

page 40

Mon.1 H 3002 Linear Complementarity Problem (Part I), Tibor Illés, Janez Povh
Marianna E.-Nagy

Tibor Illés

Janez Žerovnik

page 40

Mon.1 H 3007 Nash Equilibrium Problems and Extensions (Part I), Anna Thünen
Jan Becker

page 41

Giancarlo Bigi

Mon.1 H 0111 Conic Approaches to Infinite Dimensional Optimization (Part I), Juan Pablo Vielma
Marcelo Forets

Benoı̂t Legat

Chris Coey

page 41

Mon.1 H 0112 Recent Advances in Applications of Conic Programming, Mituhiro Fukuda, Masakazu Muramatsu, Makoto
Yamashita, Akiko Yoshise
Yuzhu Wang

Shinichi Kanoh

Sena Safarina

page 42Mon.1 H 2053 Optimization and Quantum Information, Hamza Fawzi, James Saunderson
Sander Gribling

Andreas Bluhm

Kun Fang

page 42

Tim Hoheisel

page 43

Mon.1 H 1028 Convex-Composite Optimization (Part I), Tim Hoheisel
Andre Milzarek

James Burke

Mon.1 H 1029 Recent Advances in First-Order Methods for Constrained Optimization and Related Problems(Part I), Ion Necoara, Quoc Tran-Dinh
Dirk Lorenz
Angelia Nedich
Ion Necoara
page 43
Mon.1 H 2038 Recent Advances in Derivative-Free Optimization (Part I), Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Coralia Cartis
Jeffrey Larson

Florian Jarre

page 44

Mon.1 H 3013 Applications of Multi-Objective Optimization (Part I), Gabriele Eichfelder, Alexandra Schwartz
Arun Pankajakshan

Bennet Gebken

Alexandra Schwartz

page 44

Mon.1 H 0105 Nonlinear and Stochastic Optimization (Part I), Albert Berahas, Geovani Grapiglia
Jorge Nocedal

Fred RoostaFrank E. Curtis

page 45

Mon.1 H 2032 Smooth Optimization (Part I), Dominique Orban, Michael Saunders
Elizabeth WongDavid Ek

Michael Saunders

page 45

Mon.1 H 3006 Mixed-Integer Optimal Control and PDE Constrained Optimization (Part I), Falk Hante, Christian
Kirches, Sven Leyffer
Christian Clason

Richard Krug

Falk Hante

page 46

Mon.1 H 3012 Optimization of Biological Systems (Part I), Pedro Gajardo
Anna Desilles

Victor Riquelme

Pedro Gajardo

page 46

Mon.1 H 0106 Bilevel Optimization in Image Processing (Part I), Michael Hintermüller, Kostas Papafitsoros
Juan Carlos De los Reyes

Michael Hintermüller

Kostas Papafitsoros

page 47

Mon.1 H 2013 Infinite-Dimensional Optimization of Nonlinear Systems (Part I), Fredi Tröltzsch, Irwin Yousept
Yifeng Xu
Irwin Yousept
page 47

Fredi Tröltzsch

Mon.1 H 1058 Advances in Robust Optimization (Part I), Shimrit Shtern
Krzysztof Postek

Dick den Hertog

Shimrit Shtern

page 48

Mon.1 H 3025 Advances in Optimization Under Uncertainty, Fatma Kilinc-Karzan
Paul Grigas

Anirudh Subramanyam

Prateek R Srivastava

page 48

Mon.1 H 1012 Recent Advances in First-Order Methods, Shoham Sabach
Russell Luke

Yakov Vaisbourd

Shoham Sabachpage 49

Mon.1 H 0110 Stochastic Approximation and Reinforcement Learning (Part I), Alec Koppel
Sean Meyn

Huizhen Yu

Alec Koppel

page 49

27

28

Parallel Sessions Overview

Mon.2 13:45–15:00
Mon.2 H 0107 Optimization in Healthcare (Part II), Felix Jost, Christina Schenk
Corinna Maier

Patrick-Marcel Lilienthal

Felix Jost

page 50

Mon.2 H 0104 The Interface of Generalization and Optimization in Machine Learning (Part I), Benjamin Recht, Mahdi
Soltanolkotabi
Benjamin Recht

Suriya Gunasekar [canceled]

Tengyuan Liang

page 50

Mon.2 H 3004 Optimization Methods for Machine Learning and Data Science, Shuzhong Zhang
Yin Zhang

Shuzhong Zhang

Bo Jiang

page 51

Mon.2 H 3002 Linear Complementarity Problem (Part II), Tibor Illés, Janez Povh
Zsolt Darvay

page 51

Janez Povh

Mon.2 H 3007 Nash Equilibrium Problems and Extensions (Part II), Anna Thünen
Mathias Staudigl

Matt Barker

Anna Thünen

page 52

Mon.2 H 0111 Conic Approaches to Infinite Dimensional Optimization (Part II), Juan Pablo Vielma
Lea Kapelevich

Sascha Timme

Tillmann Weisser

page 52

Mon.2 H 0112 Recent Advances and Applications of Conic Optimization, Anthony Man-Cho So
Hamza Fawzi

Huikang Liu

Anthony Man-Cho So

page 53

Mon.2 H 1028 Convex-Composite Optimization (Part II), Tim Hoheisel
Dmitry Kamzolov

page 53

Pedro Merino

Mon.2 H 1029 Recent Advances in First-Order Methods for Constrained Optimization and Related Problems(Part II), Shiqian Ma, Ion Necoara, Quoc Tran-Dinh, Yangyang Xu
Nam Ho-Nguyen
Guanghui Lan
Quoc Tran-Dinhpage 54
Mon.2 H 2038 Challenging Applications in Derivative-Free Optimization (Part I), Ana Luisa Custodio, Sébastien Le
Digabel, Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Yves Lucet
Miguel DiagoSébastien Le Digabel

page 54

Mon.2 H 3013 Applications of Multi-Objective Optimization (Part II), Gabriele Eichfelder, Alexandra Schwartz
Floriane Mefo Kue

Zahra Forouzandeh

Alberto De Marchi

page 55

Mon.2 H 0105 Nonlinear and Stochastic Optimization (Part II), Albert Berahas, Geovani Grapiglia
Alejandra Peña-Ordieres

Stefan Wild

Nikita Doikov

page 55

Mon.2 H 2032 Smooth Optimization (Part II), Dominique Orban, Michael Saunders
Dominique Orban

Marie-Ange Dahito

Douglas Gonçalves

page 56

Mon.2 H 2053 Variational Inequalities, Minimax Problems and GANs (Part I), Konstantin Mishchenko
Simon Lacoste-Julien

Dmitry Kovalev

Gauthier Gidel

page 56

Mon.2 H 3006 Mixed-Integer Optimal Control and PDE Constrained Optimization (Part II), Falk Hante, Christian
Kirches, Sven Leyffer
Sebastian Peitz

Mirko Hahn

Paul Manns

page 57

Mon.2 H 3012 Optimization of Biological Systems (Part II), Pedro Gajardo
Kenza Boumaza

page 57

Ilya Dikariev

Mon.2 H 0106 Bilevel Optimization in Image Processing (Part II), Michael Hintermüller, Kostas Papafitsoros
Elisa Davoli

Gernot Holler

Jyrki Jauhiainen

page 58

Mon.2 H 2013 Infinite-Dimensional Optimization of Nonlinear Systems (Part II), Fredi Tröltzsch, Irwin Yousept
De-Han Chen
Malte Winckler
page 58

Antoine Laurain

Mon.2 H 2033 Optimal Control of Phase Field Models, Carmen Gräßle, Tobias Keil
Tobias Keil

Masoumeh Mohammadi

Henning Bonart

page 59

Mon.2 H 1058 New Advances in Robust Optimization, Angelos Georghiou
Han YuViet Anh Nguyen

Angelos Georghiou

page 59

Mon.2 H 3025 Advances in Robust Optimization (Part II), Krzysztof Postek
Ahmadreza Marandi

Ernst Roos

Stefan ten Eikelder

page 60

Mon.2 H 1012 Nonlinear Optimization Algorithms and Applications in Data Analysis (Part I), Xin Liu, Cong Sun
Liping Wang
Cong Sun
page 60

Yaxiang Yuan

Mon.2 H 0110 Stochastic Approximation and Reinforcement Learning (Part II), Alec Koppel
Stephen Tu

Hoi-To Waipage 61Parallel Sessions Overview

Tue.1 10:30–11:45
Tue.1 H 1012 Applications in Energy (Part I)
Sauleh Siddiqui

Paolo Pisciella

Bartosz Filipecki

page 62

Tue.1 H 2013 The Interface of Generalization and Optimization in Machine Learning (Part II), Benjamin Recht, Mahdi
Soltanolkotabi
Lorenzo Rosasco

Mahdi Soltanolkotabi

Mikhail Belkinpage 62

Tue.1 H 3004 Optimization Methods for Graph Analytics, Kimon Fountoulakis, Di Wang
Nate Veldt

Rasmus Kyng

Kimon Fountoulakis

page 63

Tue.1 H 0110 Hyperbolicity Cones and Spectrahedra (Part I), Markus Schweighofer
Daniel Plaumann

Simone Naldi

Mario Kummer

page 63

Tue.1 H 2032 Methods for Sparse Polynomial Optimization, Venkat Chandrasekaran, Riley Murray
Henning Seidler

Venkat Chandrasekaran

Jie Wang

page 64

Tue.1 H 3006 Theoretical Aspects of Conic Programming, Mituhiro Fukuda, Masakazu Muramatsu, Makoto Yamashita,
Akiko Yoshise
Sunyoung Kim

Natsuki Okamura

Makoto Yamashita

page 64

Guoyin Li

page 65

Tue.1 H 1028 Recent Advances of Nonsmooth Optimization, Guoyin Li
Boris Mordukhovich

Jingwei Liang

Tue.1 H 1029 Recent Advances in First-Order Methods for Constrained Optimization and Related Problems(Part III), Ion Necoara, Quoc Tran-Dinh
Masaru Ito
Vien Van Mai
Tuomo Valkonen
page 65
Tue.1 H 2038 Derivative-Free Optimization Under Uncertainty (Part I), Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Matt Menickelly
Zaikun Zhang

Morteza Kimiaei

page 66

Tue.1 H 3013 Recent Developments in Set Optimization (Part I), Akhtar A. Khan, Elisabeth Köbis, Christiane Tammer
Niklas Hebestreit
Baasansuren Jadamba
Ernest Quintana
page 66
Tue.1 H 0105 Nonlinear and Stochastic Optimization (Part III), Albert Berahas, Geovani Grapiglia
Clément Royer

Katya Scheinberg

Daniel Robinson

page 67

Nuri Vanli

page 67

Tue.1 H 2053 Nonconvex Optimization, Asuman Ozdaglar, Nuri Vanli
Adel Javanmard

Murat Erdogdu

Tue.1 H 3025 Advances in Nonlinear Optimization With Applications (Part I), Yu-Hong Dai, Deren Han
Zhi-Long Dong

Xinxin Li

Yu-Hong Dai

page 68

Tue.1 H 3002 Optimization Applications in Mathematics of Planet Earth - Contributed Session
Sophy Oliver

page 68

Maha H. KaouriTue.1 H 0106 Computational Design Optimization (Part I), Martin Siebenborn, Kathrin Welker
Eddie Wadbro

Johannes Haubner

Kevin Sturm

page 69

Tue.1 H 0107 Optimal Control and Dynamical Systems (Part I), Cristopher Hermosilla, Michele Palladino
Teresa Scarinci

Karla Cortez

Dante Kalise

page 69

Tue.1 H 0112 Infinite-Dimensional Optimization of Nonlinear Systems (Part III), Fredi Tröltzsch, Irwin Yousept
Matthias Heinkenschloss
Moritz Ebeling-Rump
Dietmar Hömberg
page 70
Tue.1 H 1058 Models and Algorithms for Dynamic Decision Making Under Uncertainty, Man-Chung Yue
Kilian Schindler

Cagil Kocyigit

Daniel Kuhn

page 70

Tue.1 H 3012 Advances in Modeling Uncertainty via Budget-Constrained Uncertainty Sets in Robust and Distributionally Robust Optimization, Karthyek Murthy
Bradley Sturt
Omar El Housni
Rui Gao
page 71
Tue.1 H 3007 Methods and Theory for Nonconvex Optimization, Nadav HallakArmin EftekhariPeter Ochs

Nadav Hallak

page 71

Tue.1 H 0104 Stochastic Approximation and Reinforcement Learning (Part III), Alec Koppel
Andrzej Ruszczynski

Vivak Patel

Angeliki Kamoutsi

page 7229

30

Parallel Sessions Overview

Tue.2 13:15–14:30
Tue.2 H 1012 Applications in Energy (Part II)
Felipe Atenas

Gopika Geetha Jayadev

Kristina Janzen

page 73

Tue.2 H 2013 Optimization for Data Science (Part II), Ying Cui, Jong-Shi Pang
Kim-Chuan Toh

Zhengling Qi

Meisam Razaviyayn

page 73

Tue.2 H 3004 Recent Advances in Algorithms for Large-Scale Structured Nonsmooth Optimization (Part I), XudongLi
Yangjing Zhang

Xin-Yee Lam

Lei Yang

page 74

Tue.2 H 0110 Hyperbolicity Cones and Spectrahedra (Part II), Markus Schweighofer
Rainer Sinnpage 74

Markus Schweighofer

Tue.2 H 2032 Signomial Optimization and Log-Log Convexity, Venkat Chandrasekaran, Riley Murray
Berk Ozturk

Riley Murray

Akshay Agrawal

page 75

Tue.2 H 3006 Solving Saddle-Point Problems via First-Order Methods, Robert Freund
Renbo Zhao [canceled]

Panayotis Mertikopoulos

Yangyang Xu

page 75

Tue.2 H 1028 Disjunctive Structures in Mathematical Programming, Matúš Benko
Patrick Mehlitz

Matúš Benko

Michal Červinka

page 76

Tue.2 H 1029 Recent Advances in First-Order Methods for Constrained Optimization and Related Problems(Part IV), Ion Necoara, Quoc Tran-Dinh
Stephen Becker
Qihang Lin
Phan Tu Vuong
page 76
Tue.2 H 2038 Recent Advances in Derivative-Free Optimization (Part II), Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Francesco RinaldiClément Royer

Jan Feiling

page 77

Tue.2 H 3002 Convergence Analysis and Kurdyka-Lojasiewicz Property, Ting Kei Pong
Peiran Yu

Rujun Jiang

Tran Nghia

page 77

Tue.2 H 3013 Recent Developments in Set Optimization (Part II), Akhtar A. Khan, Elisabeth Köbis, Christiane Tammer
Christiane Tammer
Tamaki Tanaka
Marius Durea
page 78
Tue.2 H 0104 Recent Advances in First-Order Methods (Part I), Guanghui Lan, Yuyuan Ouyang, Yi Zhou
Renato Monteiro

William Hager

Dmitry Grishchenko

page 78

Tue.2 H 0105 Nonlinear and Stochastic Optimization (Part IV), Albert Berahas, Geovani Grapiglia
Michael Overton

Anton RodomanovAlbert Berahas

page 79

Tue.2 H 2053 Interplay of Linear Algebra and Optimization (Part I), Robert Luce, Yuji Nakatsukasa
Henry Wolkowicz

Dominik Garmatter

Robert Lucepage 79

Margherita Porcelli

page 80

Michael Garstka

page 80

Tue.2 H 3025 Advances in Interior Point Methods, Coralia Cartis
Renke Kuhlmann

Andre Tits

Tue.2 H 0111 Computational Optimization in Julia, Abel Soares Siqueira
Maxim Demenkov

Mattias Fält

Tue.2 H 0106 Computational Design Optimization (Part II), Martin Siebenborn, Kathrin Welker
Volker Schulz

Kathrin WelkerPeter Gangl

page 81Tue.2 H 0107 Recent Advances in PDE Constrained Optimization (Part I), Axel KrönerSimon Funke

Constantin Christof

Axel Kröner

page 81Tue.2 H 0112 Optimal Control and Dynamical Systems (Part II), Cristopher Hermosilla, Michele Palladino
Nathalie T. Khalil

Luis Briceño-Arias

Daria Ghilli

page 82

Tue.2 H 1058 Advances in Data-Driven and Robust Optimization, Velibor Misic
Vishal Gupta

Andrew Li

Velibor Misic

page 82

Tue.2 H 3012 Applications of Data-Driven Robust Design, Man-Chung Yue
Ihsan Yanikoglu

Shubhechyya Ghosal

Marek Petrik

page 83

Tue.2 H 3007 Nonlinear Optimization Algorithms and Applications in Data Analysis (Part II), Xin Liu, Cong Sun
Xin Liu
Xiaoyu Wang
Yanfei Wang
page 83
Tue.2 H 2033 Large-Scale Stochastic First-Order Optimization (Part I), Filip Hanzely, Samuel HorvathZheng Qu

Pavel Dvurechensky

Xun Qian

page 84

Parallel Sessions Overview

Tue.3 14:45–16:00
Tue.3 H 1012 Applications in Energy (Part III)Güray Kara

Clovis Gonzaga

Alberto J. Lamadrid L.

page 85

Tue.3 H 0105 Recent Advancements in Optimization Methods for Machine Learning (Part I), Albert Berahas, Paul
Grigas, Martin Takáč
Alexandre d’Aspremont

Heyuan Liu

Lin Xiao

page 85

Tue.3 H 0110 Quasi-Variational Inequalities and Generalized Nash Equilibrium Problems (Part I), Amal Alphonse,
Carlos Rautenberg
Christian Kanzow

Steven-Marian Stengl

Rafael Arndt

page 86

Tue.3 H 2032 Semidefinite Programming: Solution Methods and Applications, Angelika Wiegele
Marianna De Santis

Christoph Helmberg

Georgina Hall

page 86

Tue.3 H 3006 Advances in Interior Point Methods for Convex Optimization, Etienne de Klerk
Mehdi Karimi

Riley Badenbroek

David Papp

page 87

Tue.3 H 1028 Operator Splitting and Proximal Decomposition Methods, Patrick L. Combettes, Jonathan Eckstein
Yu DuMaicon Marques Alves
Jonathan Eckstein
page 87
Tue.3 H 1029 Matrix Optimization Problems: Theory, Algorithms and Applications, Chao Ding
Xinyuan Zhao

Xudong Li

Chao Ding

page 88

Tue.3 H 2038 Emerging Trends in Derivative-Free Optimization (Part I), Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Christine Shoemaker
Nikolaus Hansen

Giacomo Nannicini

page 88

Tue.3 H 3002 Recent Developments in Structured Difference-Of-Convex Optimization, Zirui Zhou
Akiko Takeda

page 89

Hongbo Dong [canceled]

Tue.3 H 3013 Recent Developments in Set Optimization (Part III), Akhtar A. Khan, Elisabeth Köbis, Christiane Tammer
Markus Arthur Köbis
Elisabeth Köbis
Akhtar A. Khan
page 89
Tue.3 H 0104 Recent Advances in First-Order Methods (Part II), Guanghui Lan, Yuyuan Ouyang, Yi Zhou
Mert Gurbuzbalaban

Hilal Asi

Damien Scieurpage 90

Tue.3 H 2013 Stochastic Methods for Nonsmooth Optimization, Konstantin Mishchenko
Tianyi Lin

Adil Salim

Konstantin Mishchenko

page 90

Tue.3 H 2053 Interplay of Linear Algebra and Optimization (Part II), Robert Luce, Yuji Nakatsukasa
Yuji Nakatsukasa

Zsolt Csizmadia

John Pearson

page 91

Daniel Rehfeldt

page 91

Tue.3 H 0111 Advances in Linear Programming
Julian Hall

Ivet Galabova

Tue.3 H 0106 Computational Design Optimization (Part III), Martin Siebenborn, Kathrin Welker
Estefania Loayza-Romero

Veronika Schulze

Christian Kahle

page 92

Tue.3 H 0107 Optimal Control and Dynamical Systems (Part III), Cristopher Hermosilla, Michele Palladino
Matthias Knauer

Tan Cao [canceled]

Gerardo Sanchez Licea

page 92

Tue.3 H 0112 Recent Advances in PDE Constrained Optimization (Part II), Axel Kröner
Meenarli Sharma

Johanna Katharina Biehl

Johann Michael Schmitt

page 93

Tue.3 H 1058 Distributionally Robust Optimization: Computation and Insights, Karthik Natarajan
Jianqiang Cheng [canceled]

Divya Padmanabhan

Karthik Natarajan

page 93

Tue.3 H 3012 Distributionally Robust Optimization, Soroosh Shafieezadeh Abadeh
Tobias Sutter

Nian Si

Soroosh Shafieezadeh Abadeh

page 94

Tue.3 H 3007 Continuous Systems in Information Processing (Part I), Martin Benning
Elena Celledoni

Robert Tovey

Martin Benningpage 94

Tue.3 H 2033 Large-Scale Stochastic First-Order Optimization (Part II), Filip Hanzely, Samuel Horvath
Nidham Gazagnadou

Hamed Sadeghi [canceled]

Samuel Horvath

page 95

31

32

Parallel Sessions Overview

Tue.4 16:30–17:45
Tue.4 H 0105 Reflections on the Life and Work of Andrew R. Conn
More information about this memorial session on page 15.

page 96

Tue.4 H 1028 Applications in Energy (Part IV)
Julien Vaes

Harald Held

Orcun Karaca

page 96

Tue.4 H 2013 Recent Advancements in Optimization Methods for Machine Learning (Part II), Albert Berahas, Martin
TakáčSebastian U. StichMartin Takáč

Nicolas Loizou

page 97

Tue.4 H 3004 Recent Advances in Algorithms for Large-Scale Structured Nonsmooth Optimization (Part II), XudongLi
Ling Liang

Meixia Lin

Krishna Pillutla

page 97

Tue.4 H 0110 Semismoothness for Set-Valued Mappings and Newton Method (Part I), Helmut Gfrerer
Ebrahim Sarabi

Jiri Outrata

Helmut Gfrerer

page 98

Timo de Wolff

page 98

Tue.4 H 2032 Advances in Polynomial Optimization, Luis Zuluaga
Olga Kuryatnikova

Benjamin Peters

Tue.4 H 3006 Geometry and Algorithms for Conic Optimization, Fatma Kilinc-Karzan
Levent Tuncel

Leonid Faybusovich

Fatma Kilinc-Karzan

page 99

Tue.4 H 1029 Quasi-Newton-Type Methods for Large-Scale Nonsmooth Optimization, Andre Milzarek, Zaiwen Wen
Ewout van den Berg
Minghan Yang
Florian Mannel
page 99
Tue.4 H 3002 Performance Estimation of First-Order Methods, François Glineur, Adrien Taylor
Yoel Drori

Donghwan Kim

François Glineur

page 100

Tue.4 H 2038 Challenging Applications in Derivative-Free Optimization (Part II), Ana Luisa Custodio, Sébastien Le
Digabel, Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Mark Abramson
Miguel Munoz Zuniga

Phillipe Rodrigues Sampaio

page 100

Tue.4 H 1012 Techniques in Global Optimization, Jon Lee, Emily Speakman
Daphne Skipper

Claudia D’Ambrosio

Leo Liberti

page 101

Tue.4 H 3013 Recent Developments in Set Optimization (Part IV), Akhtar A. Khan, Elisabeth Köbis, Christiane Tammer
Radu Strugariu
Teodor Chelmus
Diana Maxim
page 101
Tue.4 H 0111 Computational Optimization
Steven DiamondSpyridon Pougkakiotis

Harun Aydilek

page 102

Tue.4 H 0106 Computational Design Optimization (Part IV), Martin Siebenborn, Kathrin Welker
Martin Berggren

Martin Siebenborn

Christian Vollmann

page 102

Tue.4 H 0107 Optimal Control and Dynamical Systems (Part IV), Cristopher Hermosilla, Michele Palladino
Maria Soledad Aronna

Jorge Becerril

Michele Palladino

page 103

Tue.4 H 0112 Fractional/Nonlocal PDEs: Applications, Control, and Beyond (Part I), Harbir Antil, Carlos Rautenberg,
Mahamadii Warma
Ekkehard Sachs

Mahamadii Warma

Carlos Rautenberg

page 103

Tue.4 H 1058 Recent Advances in Distributionally Robust Optimization, Peyman Mohajerin EsfahaniJose Blanchet

John Duchi

Peyman Mohajerin Esfahani

page 104

Tue.4 H 2053 Robust Optimization: Theory and Applications, Vineet Goyal
Chaithanya Bandi

Daniel Zhuoyu Long

Vineet Goyal

page 104

Tue.4 H 3012 Embracing Extra Information in Distributionally Robust Models, Rui Gao
Zhenzhen Yan

Mathias Pohl

Zhiping Chen

page 105

Tue.4 H 2033 Sparse Optimization and Information Processing - Contributed Session I, Shoham Sabach
Nikolaos Ploskas

Jordan Ninin

Montaz Ali

page 105

Tue.4 H 3007 Nonlinear Optimization Algorithms and Applications in Data Analysis (Part III), Xin Liu, Cong Sun
Geovani Grapiglia
Yadan Chen
page 106Parallel Sessions Overview

Wed.1 11:30–12:45
Wed.1 H 3004 Structural Design Optimization (Part I), Tamás Terlaky
Jacek Gondzio

Alexander Brune

Tamás Terlaky

page 107

Wed.1 H 3025 Mathematical Optimization in Signal Processing (Part I), Ya-Feng Liu, Zhi-Quan Luo
Sergiy A. Vorobyov

Yang Yang

Bo Jiang

page 107

Wed.1 H 0104 Distributed Algorithms for Constrained Nonconvex Optimization: Clustering, Network Flows, and
Two-Level Algorithms, Andy Sun, Wotao Yin
Kaizhao Sun
Tsung-Hui Chang
Tao Jiang
page 108
Wed.1 H 0110 Modern Optimization Methods in Machine Learning, Qingna Li
Chengjing Wang

Peipei Tang

Qingna Li

page 108

Wed.1 H 3008 Complementarity and Variational Inequalities - Contributed Session I
Gayatri Pany

Pankaj Gautam

Felipe Larapage 109

Wed.1 H 3013 Semismoothness for Set-Valued Mappings and Newton Method (Part II), Helmut Gfrerer
Martin Brokate

Nico Strasdat

Anna Walter

page 109

Wed.1 H 0107 Semidefinite Approaches to Combinatorial Optimization, Renata Sotirov
Hao HuChristian Truden

Daniel Brosch

page 110

Grigoriy Blekherman

page 110

Wed.1 H 2032 Polynomial Optimization (Part I), Grigoriy Blekherman
Oguzhan Yuruk

João Gouveia

Wed.1 H 1028 New Frontiers in Splitting Algorithms for Optimization and Monotone Inclusions (Part I), Patrick L.
Combettes
Bang Cong Vu

Saverio Salzo

Patrick L. Combettes

page 111

Wed.1 H 1058 Convex and Nonsmooth Optimization - Contributed Session I
Igor Konnov

Ali Dorostkar

Vladimir Shikhman

page 111

Wed.1 H 2033 Emerging Trends in Derivative-Free Optimization (Part II), Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Anne Auger
Ludovic Salomon

Ana Luisa Custodio

page 112

Wed.1 H 3002 Algorithmic Approaches to Multi-Objective Optimization, Gabriele Eichfelder, Alexandra Schwartz
Yousuke Araya
Christian GüntherStefan Banholzer
page 112
Wed.1 H 0105 Geometry in Non-Convex Optimization (Part I), Nicolas Boumal, André Uschmajew, Bart Vandereycken
Suvrit Sra
Nick Vannieuwenhoven
Nicolas Boumal
page 113
Wed.1 H 1012 Nonlinear Optimization Methods and Their Global Rates of Convergence, Geovani Grapiglia
Oliver Hinder

Philippe L. Toint

Ernesto G. Birgin

page 113

Wed.1 H 1029 Practical Aspects of Nonlinear Optimization, Coralia Cartis
Hiroshige Dan

Michael Feldmeier

Tim Mitchell

page 114

Wed.1 H 2013 New Trends in Optimization Methods and Machine Learning (Part I), El houcine Bergou, Aritra Dutta
Vyacheslav Kungurtsev
El houcine Bergou
Xin Lipage 114
Wed.1 H 2053 Efficient Numerical Solution of Nonconvex Problems, Serge Gratton, Elisa Riccietti
Serge Gratton

Selime Gurol

Elisa Riccietti

page 115

Wed.1 H 0106 Optimal Control of Nonsmooth Systems (Part I), Constantin Christof, Christian Clason
Daniel Walter

Lukas Hertlein

Sebastian Engelpage 115

Wed.1 H 0111 Optimal Control and Dynamical Systems (Part V), Cristopher Hermosilla, Michele Palladino
Franco Rampazzo

Peter Wolenski

Piernicola Bettiol

page 116

Wed.1 H 0112 Fractional/Nonlocal PDEs: Applications, Control, and Beyond (Part II), Harbir Antil, Carlos Rautenberg,
Mahamadii Warma
Deepanshu Vermapage 116

Benjamin Manfred Horn

Wed.1 H 3006 Statistical Methods for Optimization Under Uncertainty, Henry Lam
Fengpei Li

Huajie Qian

Yuanlu Bai

page 117

Wed.1 H 3007 Recent Development in Applications of Distributionally Robust Optimization, Zhichao Zheng
Zhenzhen Yan
Guanglin Xu [canceled]
Yini Gaopage 117
Wed.1 H 2038 Complexity of Sparse Optimization for Subspace Identification, Julien Mairal
Guillaume Garrigos

Yifan Sun

Julien Mairalpage 118

Wed.1 H 3012 Stochastic Optimization and Its Applications (Part I), Fengmin Xu
Xiaojun Chen

Jianming Shi

Fengmin Xu

page 118

33

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Parallel Sessions Overview

Wed.2 14:15–15:30
Wed.2 H 3004 Structural Design Optimization (Part II), Tamás Terlaky
Michael Stingl

Alemseged Gebrehiwot Weldeyesus

Jonas Holley

page 119Wed.2 H 3025 Mathematical Optimization in Signal Processing (Part II), Ya-Feng Liu, Zhi-Quan Luo
Zhi-Quan Luo

page 119

Ya-Feng Liu

Wed.2 H 0104 Recent Advancements in Optimization Methods for Machine Learning (Part III), Albert Berahas, Martin
TakáčPeter Richtárik

Aaron Defazio [canceled]

Aurelien Lucchi

page 120

Wed.2 H 0110 Recent Advances in Convex and Non-Convex Optimization and Their Applications in Machine
Learning (Part I), Qihang Lin, Tianbao Yang
Yiming Ying
Niao He
Javier Penapage 120
Wed.2 H 3013 Quasi-Variational Inequalities and Generalized Nash Equilibrium Problems (Part II), Amal Alphonse,
Carlos Rautenberg
Abhishek SinghJo Andrea Brüggemann

Ruchi Sandilya

page 121

Negar Soheili

page 121

Victor Magron

page 122

Wed.2 H 0107 Advances in Convex Optimization, Negar Soheili
Robert FreundMichael Friedlander

Wed.2 H 2032 Polynomial Optimization (Part II), Grigoriy Blekherman
Etienne de Klerk

Cordian Riener

Wed.2 H 1028 New Frontiers in Splitting Algorithms for Optimization and Monotone Inclusions (Part II), Patrick L.
Combettes
Calvin Wylie

Isao Yamada

Christian L. Müller

page 122

Wed.2 H 1058 Algorithms for Large-Scale Convex and Nonsmooth Optimization (Part I), Daniel Robinson
Guilherme Franca

Zhihui Zhu

Sergey Guminovpage 123

Wed.2 H 2033 Model-Based Derivative-Free Optimization Methods, Ana Luisa Custodio, Sébastien Le Digabel, Margherita
Porcelli, Francesco Rinaldi, Stefan Wild
Raghu Pasupathy
Lindon Roberts

Liyuan Cao

page 123

Syuuji Yamada

page 124

Wed.2 H 3008 Global Optimization - Contributed Session I
Kuan LuFrancesco Romito

Wed.2 H 0105 Geometry in Non-Convex Optimization (Part II), Nicolas Boumal, André Uschmajew, Bart Vandereycken
Marco Sutti
Max Pfeffer
Yuqian Zhang
page 124
Wed.2 H 1012 Developments in Structured Nonconvex Optimization (Part I), Jun-ya Gotoh, Yuji Nakatsukasa, Akiko
TakedaJun-ya Gotoh

Shummin Nakayama

Takayuki Okuno

page 125

Wed.2 H 1029 Advances in Nonlinear Optimization Algorithms (Part I)
Johannes Brust

Hardik Tankaria

Mehiddin Al-Baali

page 125

Wed.2 H 2013 New Trends in Optimization Methods and Machine Learning (Part II), El houcine Bergou, Aritra Dutta
Aritra Dutta
Michel Barlaud
Jakub Mareček
page 126
Wed.2 H 2053 Recent Trends in Large Scale Nonlinear Optimization (Part I), Michal Kočvara, Panos Parpas
Derek Driggs

Zhen Shao

Panos Parpas

page 126

Wed.2 H 0106 Optimal Control of Nonsmooth Systems (Part II), Constantin Christof, Christian Clason
Dorothee Knees

Vu Huu Nhu

Livia Betz

page 127

Wed.2 H 0111 Optimal Control and Dynamical Systems (Part VI), Cristopher Hermosilla, Michele Palladino
Paloma Schäfer Aguilar

Laurent Pfeiffer

Caroline Löbhard

page 127

Wed.2 H 0112 Decomposition-Based Methods for Optimization of Time-Dependent Problems (Part I), Matthias
Heinkenschloss, Carl Laird
Sebastian GötschelStefan Ulbrich

Denis Ridzal

page 128

Wed.2 H 3006 Recent Advances in Theories of Distributionally Robust Optimization (DRO), Zhichao Zheng
Karthyek Murthy
Jingui Xie
Grani A. Hanasusantopage 128
Wed.2 H 3007 Decision-Dependent Uncertainty and Bi-Level Optimization, Chrysanthos Gounaris, Anirudh Subramanyam
Anirudh Subramanyam
Mathieu Besançon
Omid Nohadani
page 129
Wed.2 H 2038 Primal-Dual Methods for Structured Optimization, Mathias Staudigl
Robert Ernö Csetnek

Darina Dvinskikh

Kimon Antonakopoulos

page 129

Wed.2 H 3012 Stochastic Optimization and Its Applications (Part II), Fengmin Xu
Sunho Jang

Jussi Lindgren

Luis Espath

page 130

Parallel Sessions Overview

Wed.3 16:00–17:15
Wed.3 H 2053 Applications in Finance and Real Options
Daniel Ralph

Peter Schütz

Luis Zuluaga

page 131

Wed.3 H 0104 Recent Advancements in Optimization Methods for Machine Learning (Part IV), Albert Berahas, Martin
TakáčMartin Jaggi

Robert Gower

Ahmet Alacaoglu

page 131

Wed.3 H 0110 Recent Advances in Convex and Non-Convex Optimization and Their Applications in Machine
Learning (Part II), Qihang Lin, Tianbao Yang
Yuejie Chi
Praneeth Netrapalli
Raef Bassily
page 132
Wed.3 H 3002 Recent Advances in Distributed Optimization, Pavel Dvurechensky, Cesar A Uribe
Alex Olshevsky

Alexander Gasnikov

Jingzhao Zhang

page 132

Wed.3 H 1058 Complementarity: Extending Modeling and Solution Techniques (Part I), Michael Ferris
Olivier Huber

Youngdae Kim

Michael Ferris

page 133

Wed.3 H 0111 Theoretical Aspects of Conic Optimization, Roland HildebrandNaomi Shaked-Monderer

Takashi Tsuchiya

Roland Hildebrandpage 133

Wed.3 H 2032 Conic Optimization and Machine Learning, Amir Ali Ahmadi
Pablo Parrilo [canceled]

Pravesh KothariAmir Ali Ahmadi

page 134

Wed.3 H 1028 New Frontiers in Splitting Algorithms for Optimization and Monotone Inclusions (Part III), Patrick
L. Combettes
Nimit Nimana

Dang-Khoa Nguyen

Marcel Jacobse

page 134

Wed.3 H 3025 Algorithms for Large-Scale Convex and Nonsmooth Optimization (Part II)
Masoud AhookhoshAvinash Dixit

Ewa Bednarczuk

page 135

Wed.3 H 2033 Derivative-Free Optimization Under Uncertainty (Part II), Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Fani BoukouvalaFriedrich Menhorn

Morteza Kimiaei

page 135

Wed.3 H 3008 Generalized Distances and Envelope Functions, Ting Kei Pong
Scott B. Lindstrom

Andreas Themelis

Ting Kei Pong

page 136

Mario Lezcano Casado

page 136

Wed.3 H 0105 Geometry and Optimization, Coralia Cartis
Florentin Goyens

Erlend Skaldehaug Riis

Wed.3 H 1012 Developments in Structured Nonconvex Optimization (Part II), Jun-ya Gotoh, Yuji Nakatsukasa, Akiko
TakedaNikitas Rontsis

André Uschmajew

Tianxiang Liu

page 137

Wed.3 H 1029 Advances in Nonlinear Optimization Algorithms (Part II)
Erik Berglund

Wenwen Zhou

Hao Wang

page 137

Wed.3 H 2013 New Trends in Optimization Methods and Machine Learning (Part III)
Saeed Ghadimi

page 138

Hien Le

Wed.3 H 3007 Recent Trends in Large Scale Nonlinear Optimization (Part II), Michal Kočvara, Panos Parpas
Filippo Pecci

Michal Kočvara

Man Shun Ang

page 138

Wed.3 H 0106 Optimal Control of Nonsmooth Systems (Part III), Constantin Christof, Christian Clason
Gerd Wachsmuth

Anne-Therese Rauls

Georg Müller

page 139

Wed.3 H 0107 Optimal Control and Dynamical Systems (Part VII), Cristopher Hermosilla, Michele Palladino
Emilio Vilches

Cristopher HermosillaFernando Lobo Pereira

page 139

Wed.3 H 0112 Decomposition-Based Methods for Optimization of Time-Dependent Problems (Part II), Matthias
Heinkenschloss, Carl Laird
Nicolas Gauger

Alexander Engelmann

Carl Laird

page 140

Frans de Ruiter

page 140

Wed.3 H 3004 Robust Nonlinear Optimization, Frans de Ruiter
Oleg Kelis

Jianzhe (Trevor) Zhen

Wed.3 H 3006 Algorithms and Applications of Robust Optimization, William Haskell
Napat Rujeerapaiboon

Wolfram Wiesemann

William Haskell

page 141

Wed.3 H 2038 Block Alternating Schemes for Nonsmooth Optimization at a Large Scale, Emilie Chouzenoux
Krzysztof Rutkowski
Marco Prato
Jean-Christophe Pesquet
page 141
Wed.3 H 3012 Stochastic Optimization and Its Applications (Part III), Fengmin Xu
Christian Bayer

Xiaobo Li

page 142

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Parallel Sessions Overview

Thu.1 09:00–10:15
Thu.1 H 3004 Applications in Logistics and Supply Chain Management
Okan Ozener

Mingyu Li

Ali Ekici

page 143

Thu.1 H 0105 Distributed and Decentralized Optimization: Learning, Lower-Complexity Bounds, andCommunication-Efficiency, Andy Sun, Wotao Yin
Qing Ling
Mingyi Hong
Wotao Yinpage 143
Thu.1 H 3006 Large-Scale Optimization for Statistical Learning, Paul Grigas
Rahul Mazumder [canceled]page 144

Hussein Hazimeh [canceled]Thu.1 H 3007 Big Data and Machine Learning - Contributed Session I
Scindhiya Laxmi

Robert Ravier

Guozhi Dong

page 144

Thu.1 H 3013 Complementarity: Extending Modeling and Solution Techniques (Part II), Michael Ferris
Francesco Caruso

page 145

David Stewart

Thu.1 H 2032 Methods and Applications in Mixed Integer Semidefinite Programming, Christoph Helmberg
Angelika Wiegele

Julie Sliwak

Felix Lieder

page 145

Thu.1 H 2033 Matrix Analysis and Conic Programming, Etienne de Klerk
Cécile Hautecoeur

page 146

Fei Wang

Thu.1 H 2038 Second and Higher Order Cone Programming and Trust Region Methods, Etienne de Klerk
Mirai Tanaka

Walter Gomez

Lijun Ding [canceled]page 146

Thu.1 H 1028 Splitting Methods and Applications (Part I), Pontus Giselsson, Ernest Ryu, Adrien Taylor
Francisco Javier Aragón Artacho

Sorin-Mihai Grad

Xiaoqun Zhang

page 147

Thu.1 H 3025 Emerging Trends in Derivative-Free Optimization (Part III), Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Susan Hunter
Mauro Passacantando

Evgeniya Vorontsova

page 147

Ozan Evkaya

page 148

Thu.1 H 1029 Global Optimization - Contributed Session II
Majid DarehmirakiJosé Fernández

Thu.1 H 0111 Uncertainty in Multi-Objective Optimization, Gabriele Eichfelder, Alexandra Schwartz
Fabian Chlumsky-Harttmann

page 148

Patrick Groetzner

Thu.1 H 0104 Recent Advances of First-Order Methods for Nonlinear Problems (Part I), Shiqian Ma, Yangyang Xu
Haihao Lu
Tianbao Yang
N. Serhat Aybat
page 149
Thu.1 H 0106 Optimality Conditions in Nonlinear Optimization (Part I), Gabriel Haeser
Alexey Tret’yakov

Bruno Figueira LourençoPaulo J. S. Silva

page 149

Thu.1 H 0107 Methods of Optimization in Riemannian Manifolds (Part I), Orizon Pereira FerreiraJoão Xavier da Cruz Neto

João Carlos Souza

Maurı́cio Louzeiro

page 150

Thu.1 H 2053 Mixed-Integer Optimal Control and PDE Constrained Optimization (Part III), Falk Hante, Christian
Kirches, Sven Leyffer
Clemens Zeile

Bart van Bloemen Waanders

Sven Leyffer

page 150

Thu.1 H 0112 Geometric Methods in Optimization of Variational Problems (Part I), Roland Herzog, Anton Schiela
Ronny Bergmann
Robin Richter
Dominik Stöger
page 151
Thu.1 H 2013 PDE-constrained Optimization Under Uncertainty (Part I), Harbir Antil, Drew Philip Kouri, Thomas Michael
Surowiec, Michael Ulbrich, Stefan UlbrichRené Henrion
Harbir AntilPhilipp Guthpage 151

Thu.1 H 1058 Robust Optimization: New Advances in Methodology and Applications, Nikos Trichakis
Lukáš Adam [canceled]

Phebe Vayanos

Nikos Trichakis

page 152

Thu.1 H 1012 Sparse Optimization and Information Processing - Contributed Session II, Shoham SabachRadu-Alexandru Dragomir

Yue Xie

Mustafa C. Pinar

page 152

Thu.1 H 3012 Advances in Probabilistic/Chance Constrained Problems
Yassine LaguelPedro Pérez-Arospage 153

Parallel Sessions Overview

Thu.2 10:45–12:00
Thu.2 H 3004 Applications in Economics
Benteng Zou

Asiye Aydilek

Po-An Chen

page 154

Thu.2 H 3012 Applications in Engineering
Dimitrios Nerantzis

page 154

Manuel RadonsThu.2 H 0105 Nonconvex Optimization in Machine Learning (Part I), Ethan Fang
Yuxin Chen

Zhuoran Yang

Alfonso Lobos

page 155

Thu.2 H 0110 Recent Advances in Optimization Techniques for Large-Scale Structured Problems, Akiko Takeda
Zirui Zhou
Michael R. Metel
Pierre-Louis Poirion
page 155
Thu.2 H 3007 Big Data and Machine Learning - Contributed Session II
Lei ZhaoSarit Khirirat

Rachael Tappenden

page 156

Thu.2 H 3013 Numerical Methods for Bilevel Optimization and Applications (Part I), Alain Zemkoho
Anthony Dunn

Andrey Tin

Alain Zemkoho

page 156

Thu.2 H 2032 Advances in Algorithms for Conic Optimization, Julio C Goez
Joachim Dahl

Alex Wang

Julio C Goez [canceled]page 157

Thu.2 H 2033 Polynomial Optimization Algorithms, Software, and Applications, Etienne de Klerk
Brais González Rodrı́guez

Yi-Shuai Niu

Anders Eltved

page 157

Konrad Schrempf

page 158

Thu.2 H 2038 Algorithms for Conic Programming, Etienne de Klerk
Chee Khian Sim

Kresimir Mihic

Thu.2 H 1028 Splitting Methods and Applications (Part II), Francisco Javier Aragón Artacho, Matthew K. Tam
C.H. Jeffrey Pang

Pontus Giselsson

Adrien Taylor

page 158

Thu.2 H 1029 Convex and Nonsmooth Optimization - Contributed Session II
Nima Rabiei

Maryam Yashtini

Jose Vidal-Nunez

page 159

Thu.2 H 3025 Recent Advances in Derivative-Free Optimization (Part III)Tyler Chang

Alexander Matei

Douglas Vieira

page 159

Thu.2 H 0104 Recent Advances of First-Order Methods for Nonlinear Problems (Part II), Shiqian Ma, Ion Necoara,
Quoc Tran-Dinh, Yangyang Xu
Hao YuRunchao Ma

Yuyuan Ouyang

page 160

Thu.2 H 0106 Optimality Conditions in Nonlinear Optimization (Part II)
Olga Brezhneva

Maria Daniela Sanchez

S. K. Gupta

page 160

Thu.2 H 2053 Methods of Optimization in Riemannian Manifolds (Part II), Orizon Pereira Ferreira
Josua Sassen

page 161

Orizon Pereira FerreiraThu.2 H 0112 Geometric Methods in Optimization of Variational Problems (Part II), Roland Herzog, Anton Schiela
Andreas Weinmann
Thomas Vogt
Gladyston de Carvalho Bento
page 161
Thu.2 H 2013 PDE-constrained Optimization Under Uncertainty (Part II), Harbir Antil, Drew Philip Kouri, Thomas
Michael Surowiec, Michael Ulbrich, Stefan UlbrichThomas Michael Surowiec
Ekaterina Kostina

Andreas Van Barel

page 162

Thu.2 H 3006 A Mixed Bag of Decision Problems, Peyman Mohajerin Esfahani
Man-Chung Yue

Bart P.G. Van Parys

Zhi Chen

page 162

Thu.2 H 1012 Continuous Systems in Information Processing (Part II), Martin Benning
Jan LellmannNoémie DebrouxDaniel Tenbrinck

page 163

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38

Parallel Sessions Overview

Thu.3 13:30–14:45
Thu.3 H 3004 Other Applications
Sugandima Mihirani Vidanagamachchi

Yidiat Omolade Aderinto

Jean-Paul Arnaout

page 164

Thu.3 H 0105 Canceled! Nonconvex Optimization in Machine Learning (Part II), Ethan Fang
Stephane Chretien [canceled]page 164

Ke Wei [canceled]Thu.3 H 3007 Big Data and Machine Learning - Contributed Session III
Qing Qu

Cheolmin Kim

Dimosthenis Pasadakis

page 165

Thu.3 H 3013 Numerical Methods for Bilevel Optimization and Applications (Part II), Alain Zemkoho
Thomas Kleinert

Tangi Migot

Jie Jiang

page 165

Thu.3 H 2033 Miscellaneous Topics in Conic Programming, Etienne de Klerk
Antonios Varvitsiotis

Lucas Slot

Sandra S. Y. Tanpage 166

Thu.3 H 2038 Conic Optimization Approaches to Discrete Optimization Problems, Etienne de Klerk
Fatima Ezzahra Khalloufi

Timotej Hrga

Enrico Bettiol

page 166

Thu.3 H 1028 Splitting Methods and Applications (Part III), Pontus Giselsson, Ernest Ryu, Adrien Taylor
Puya Latafat

Matthew K. Tam

Ernest Ryu

page 167

Thu.3 H 1029 Convex and Nonsmooth Optimization - Contributed Session III
Ching-pei Lee

Yan GuAndrew Eberhard

page 167

Thu.3 H 0104 Advances in Nonlinear Optimization With Applications (Part II), Yu-Hong Dai, Deren Han
Xingju Cai

Caihua Chen

Deren Han [canceled]page 168

Refail Kasimbeyli

page 168

Thu.3 H 0106 Nonlinear Optimization - Contributed Session III
Gulcin Dinc Yalcin

Rafael Durbano Lobato

Thu.3 H 0107 Variational Inequalities, Minimax Problems and GANs (Part II), Konstantin Mishchenko
Daoli Zhu

Sarath Pattathil

Georgios Piliouras

page 169

Jourdain Lamperski

page 169

Raul Tempone

page 170

Thu.3 H 2032 Nonlinear Optimization - Contributed Session II
Simone Rebegoldi

Andrea CristofariThu.3 H 2053 Nonlinear Optimization - Contributed Session I
Truc-Dao Nguyen

Adrian HauswirthThu.3 H 0112 Geometric Methods in Optimization of Variational Problems (Part III), Roland Herzog, Anton Schiela
Anton Schiela
Silvio Fanzon
Hanne Hardering
page 170
Thu.3 H 2013 PDE-constrained Optimization Under Uncertainty (Part III), Harbir Antil, Drew Philip Kouri, Thomas
Michael Surowiec, Michael Ulbrich, Stefan Ulbrich
Sergio Rodrigues
Johannes Milz

page 171

Thu.3 H 1058 Distributionally Robust Optimization: Advances in Methodology and Applications, Aurelie Thiele
Sanjay Mehrotra
Tito Homem-de-Mello
Aurelie Thiele
page 171
Thu.3 H 3006 Dynamic Optimization Under Data Uncertainty, Omid Nohadani
Sebastian Pokutta

Eojin Han

Dan Iancu

page 172

Thu.3 H 1012 Sparse Optimization and Information Processing - Contributed Session III, Shoham Sabach
Ryan Cory-Wright

Bolin Pan

Bogdan Toader

page 172

Thu.3 H 3012 Solution and Analysis of Stochastic Programming and Stochastic Equilibria
Pedro Borges

Shu Lupage 173

39

Mon.1 11:00–12:15
APP

BIG

Mon.1 H 0107
Optimization in Healthcare (Part I)
Organizers: Felix Jost, Christina Schenk
Chair: Felix Jost

Mon.1 H 0104
Optimization for Data Science (Part I)
Organizers: Ying Cui, Jong-Shi Pang
Chair: Jong-Shi Pang

Marta Sauter, University of Heidelberg (joint work with Hans
Georg Bock, Kathrin Hatz, Ekaterina Kostina, Katja Mombaur,
Johannes P. Schlöder, Sebastian Wolf)
Simultaneous Direct Approaches for Inverse Optimal Control Problems and Their Application to Model-Based Analysis of Cerebral Palsy Gait

Jong-Shi Pang, University of Southern California (joint workwith Ying Cui, Ziyu He)
Multi-composite Nonconvex Optimization for Learning
Deep Neural Network

Inverse optimal control problems arise in the field of modeling, simulation and optimization of the human gait. For ourstudies we consider a bilevel optimization problem. The lower
optimal control model includes unknown parameters that haveto be determined by fitting the model to measurements, which
is done in the upper level. We present mathematical and numerical methods using a simultaneous direct approach for inverse
optimal control problems and derive optimal control models forthe gait of patients with cerebral palsy from real-world motion
capture data.
Leonard Wirsching, Heidelberg University (joint work with
Hans Georg Bock, Ekaterina Kostina, Johannes P. Schlöder,Andreas Sommer)
Solution and Sensitivity Generation for Differential Equations with State-Dependent Switches – an ApplicationOriented Approach
For ODEs with switched or non-smooth dynamics, modelers
often provide code containing conditionals like IF, ABS, MIN,
and MAX. Standard black-box integrators, in general, showpoor performance or may compute wrong dynamics without
notice. Transformation into model descriptions suitable forstate-of-the-art methods is often cumbersome or infeasible. In
this talk, we present a new approach that works on the dynamic
model equations as provided by the modeler and allows to treat
switched and non-smooth large-scale dynamics efficiently andaccurately for both integration and sensitivity generation.
Christina Schenk, Carnegie Mellon University (joint work withLorenz Biegler, Lu Han, Jason Mustakis)
Kinetic Parameter Estimation Based on Spectroscopic
Data and Its Application to Drug Manufacturing Processes
The development of drug manufacturing processes involves
dealing with spectroscopic data. In many cases kinetic parameter estimation from spectra has to be performed without
knowing the absorbing species in advance, such that they haveto be estimated as well. That is why we take a closer look at
the development of optimization-based procedures in order toestimate the variances of system and measurement noise andthus determine the concentration and absorbance profiles, andparameters simultaneously. The further investigations related
to the application specific challenges are presented.

We present in this paper a novel deterministic algorithmic
framework that enables the computation of a directional stationary solution of the empirical deep neural network training
problem formulated as a multi-composite optimization problem
with coupled nonconvexity and nondifferentiability. This is thefirst time to our knowledge that such a sharp kind of stationary
solutions is provably computable for a nonsmooth deep neural network. Numerical results from a matlab implementation
demonstrate the effectiveness of the framework for solving
reasonably sized networks.
Defeng Sun, The Hong Kong Polytechnic University
A majorized proximal point dual Newton algorithm for nonconvex statistical optimization problems
We consider high-dimensional nonconvex statistical optimization problems such as the regularized square-root Lasso
problem, where in the objective the loss terms are possibly
nonsmooth or non-Lipschitzian and the regularization terms are
the difference of convex functions. We introduce a majorized
proximal point dual Newton algorithm (mPPDNA) for solving
these large scale problems. We construct proper majorized
proximal terms that are conducive to the design of an efficient
dual based semismooth Newton method of low computational
complexities. Extensive numerical results are provided.
Gesualdo Scutari, Purdue University (joint work with Guang
Cheng, Ying Sun, Ye Tian)
Distributed inference over networks: geometrically convergent algorithms and statistical guarantees
We study distributed high-dimensional M-estimation problems over networks, modeled as arbitrary graphs (with no centralized node). We design the first class of distributed algorithms with convergence guarantees for such a class of Empirical
Risk Minimization (ERM) problems. We prove that, under mild
technical assumptions, the proposed algorithm converges at alinear rate to a solution of the ERM that is statistically optimal.
Furthermore, for a fixed network topology, such a linear rateis invariant when the problem dimension scales properly withrespect to the sample size.

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BIG

VIS

Mon.1 H 3004
Non-Convex Optimization for Neural Networks
Organizers: Mingyi Hong, Ruoyu Sun
Chair: Mingyi HongMon.1 H 3002
Linear Complementarity Problem (Part I)
Organizers: Tibor Illés, Janez Povh
Chair: Janez Povh

Ruoyu Sun, UIUC
Landscape of Over-parameterized Networks

Marianna E.-Nagy, Budapest University of Technology andEconomics
Constructing and analyzing sufficient matrices

Non-convexity of neural networks may cause bad local minima, but the recent guess is that over-parameterization cansmooth the landscape. We prove that for any continuous activation functions, the loss function has no bad strict local
minimum, both in the regular sense and in the sense of sets.
This result holds for any convex and continuous loss function,and the data samples are only required to be distinct in at least
one dimension. Furthermore, we show that bad local minima
do exist for a class of activation functions.
Sijia Liu, MIT-IBM Watson AI Lab, IBM Research (joint workwith Mingyi Hong)On the convergence of a class of adam-type algorithms for
non-convex optimization
We study a class of adaptive gradient based momentum
algorithms, called “Adam-type” algorithm, that update search
directions and learning rate simultaneously using past gradients.
The convergence of these algorithms for nonconvex problems
remains an open question. We provide a set of mild sufficientconditions that guarantee the convergence of the Adam-type
methods. We prove that under our derived conditions, thesemethods can achieve convergence rate O(log(T )/ sqrt(T )) for
nonconvex optimization. We show the conditions are essential
in the sense that violating them may cause divergence.
Alex Schwing, University of Illinois at Urbana-Champaign
Optimization for GANs via Sliced DistanceIn the Wasserstein GAN, the Wasserstein distance is commonly transformed to its dual via the Kantorovich-Rubinstein
Duality and then optimized. However, the dual problem requires a Lipschitz condition which is hard to impose. One idea
is to solve the primal problem directly, but this is computationally expensive and has an exponential sample complexity. We
propose to solve the problem using the sliced and max-sliced
Wasserstein distance, which enjoy polynomial sample complexity. Analysis shows that this approach is easy to train, requires
little tuning, and generates appealing results.

The sufficient matrix class was defined related to Linear
complementarity problems. This is the widest class for which thecriss-cross and the interior point algorithms (IPM) for solving
LCPs are efficient in some sence. It is an NP-complete problem
to decide whether a matrix belongs to this class. Therefore,
we build a library of sufficient matrices to provide a test set foralgorithms solving such LCPs. We show different techniques
to generate such matrices. Furthermore, we present results
about the handicap, which matrix parameter is important for
the theoretical complexity of the IPM.
Tibor Illés, Budapest University of Technology and Economics,
Budapest, Hungary
Interior point algorithms for general linear complementarity problems
Linear Complementarity Problems (LCP) belong to the classof NP-complete problems. Darvay et al. (2018) showed thatpredictor-corrector (PC) interior point algorithms (IPA) using
algebraic equivalent transformation (AET) solves sufficient
LCPs in polynomial time. Our aim is to construct new PC
IPAs based on AETs which in polynomial time either give an-optimal solution of the general LCP or detect the lack ofsufficiency property. In the latter case, the algorithms givea polynomial size certificate depending on parameter κ, theinitial interior point and the input size of the (LCP).
Janez Žerovnik, University of Ljubljana (joint work with Tibor
Illés, Janez Povh)
On sufficient properties for sufficient matrices
In this talk we present new necessary and sufficient conditions
for testing matrix sufficiency, which is an NP-hard problem. We
discuss methods for testing matrix sufficiency based on the new
characterizations and propose an algorithm with exponential
complexity which in its kernel solves many simple instancesof linear programming problems. Numerical results confirm
relevance of this algorithm.

VIS
Mon.1 H 3007
Nash Equilibrium Problems and Extensions (Part I)
Organizer: Anna Thünen
Chair: Anna Thünen
Jan Becker, TU Darmstadt (joint work with Alexandra
Schwartz)
Linear-quadratic multi-leader-follower games in Hilbert
spaces41

CON
Mon.1 H 0111
Conic Approaches to Infinite Dimensional Optimization(Part I)
Organizer: Juan Pablo Vielma
Chair: Juan Pablo Vielma
Marcelo Forets, Universidad de la República
Algorithms for Flowpipe Approximation using Sum-ofSquares

Multi-leader-follower games (MLFGs) can be seen as anhierarchical extension of Nash equilibrium problems. By now,
the analysis of the latter is well-known and well-understoodeven in infinite dimensions. While the analysis of MLFGs infinite dimensions is restricted to special cases, the analysis
in function spaces is still in its infancy. Therefore, the talkfocuses on linear-quadratic MLFGs in Hilbert spaces and theircorresponding equilibrium problems with complementarity constraints (EPCCs). By considering relaxed equilibrium problems,
we derive a novel equilibrium concept for MLFGs.

Exhaustive coverage of all possible behaviours is importantwhen we model safety-critical or mission-critical systems, either
embedded or cyber-physical, since uncertain inputs, disturbances or noise can have an adverse effect in performance andreliability. In this talk we survey methods that have emerged
recently to soundly verify properties of systems using sum-ofsquares optimisation methods. The study is conducted with
our software tool JuliaReach that gives a unified user interface implementing different reachability approaches while notsacrificing features or performance.

Giancarlo Bigi, Università di Pisa (joint work with Simone
Sagratella)
Algorithms for generalized Nash games in semi-infinite programming

Benoı̂t Legat, UCLouvain (joint work with Raphaël Jungers,
Paulo Tabuada)Computing polynomial controlled invariant sets using Sumof-Squares Programming

Semi-infinite programs (SIPs) can be formulated as generalized Nash games (GNEPs) with a peculiar structure under mild
assumptions. Pairing this structure with a penalization scheme
for GNEPs leads to methods for SIPs. A projected subgradientmethod for nonsmooth optimization and a subgradient method
for saddlepoints are adapted to our framework, providing two
kinds of the basic iteration for the penalty scheme. Beyond
comparing the two algorithms, these results and algorithms are
exploited to deal with uncertainty and analyse robustness in
portfolio selection and production planning.

We develop a method for computing controlled invariant setsof discrete-time affine systems using Sum-of-Squares programming. We apply our method to the controller design problem
for switching affine systems with polytopic safe sets but ourwork also improves the state of the art for the particular caseof LTI systems. The task is reduced to a Sum-of-Squares programming problem by enforcing an invariance relation in the
dual space of the geometric problem.
Chris Coey, MIT OR Center (joint work with Lea Kapelevich,Juan Pablo Vielma)Nonsymmetric conic optimization algorithms and applications
Hypatia is an open-source optimization solver in Julia forprimal-dual conic problems involving general convex cones.
Hypatia makes it easy to define and model with any closed
convex conic set for which an appropriate primal or dual barrier
function is known. Using novel nonsymmetric cones, we model
control applications with smaller, more natural formulations,and demonstrate Hypatia’s superior performance (relative toother state-of-the-art conic interior point solvers) on several
large problems.

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CON
Mon.1 H 0112
Recent Advances in Applications of Conic Programming
Organizers: Mituhiro Fukuda, Masakazu Muramatsu, Makoto
Yamashita, Akiko Yoshise
Chair: Makoto Yamashita
Yuzhu Wang, University of Tsukuba (joint work with Akihiro
Tanaka, Akiko Yoshise)
Polyhedral Approximations of the Semidefinite Cone andTheir Applications
Large and sparse polyhedral approximations of the semidefinite cone have been showed important for solving hard conic
optimization problems. We propose new approximations that
contain the diagonally dominant cone and are contained in thescaled diagonally dominant cone, by devising a simple expansion of the semidefinite bases. As their applications, we propose
1. methods for identifying elements in certain cones, 2. cutting
plane methods for conic optimization and 3. a Lagrangian-DNN
method for some quadratic optimization problems. Numericalresults showed the efficiency of these methods.
Shinichi Kanoh, University of Tsukuba (joint work with Akiko
Yoshise)A centering ADMM for SDP and its application to QAP
We propose a new method for solving SDP problems based on
primal dual interior point method and ADMM, called Centering
ADMM (CADMM). CADMM is an ADMM but it has thefeature of updating the variables toward the central path. We
conduct numerical experiments with SDP relaxation problems
of QAP and compare CADMM with ADMM. The results
demonstrate that which of two methods is favorable depends
on instances.
Sena Safarina, Tokyo Institute of Technology (joint work withTim J. Mullin, Makoto Yamashita)
A Cone Decomposition Method with Sparse Matrix for
Mixed-Integer SOCP problem
Our research is focused on an optimal contribution selection(OCS) problem in equal deployment, which is one of the optimization problems that aims to maximize the total benefit
under a genetic diversity constraint. The constraint allows the
OCS problem to be modeled as mixed-integer second-ordercone programming (MI-SOCP). We proposed a cone decomposition method (CDM) that is based on a geometric cut incombination with a Lagrangian multiplier method. We alsoutilized the sparsity found in OCS, into a sparse linear approximation, that can strongly enhance the performance of CDM.

CON
Mon.1 H 2053
Optimization and Quantum Information
Organizers: Hamza Fawzi, James Saunderson
Chair: Hamza Fawzi
Sander Gribling, CWI, Amsterdam (joint work with Monique
Laurent, David de Laat)
Quantum graph parameters and noncommutative polynomial optimization
Graph parameters such as the chromatic number can beformulated in several ways. E.g., as polynomial optimization
problems or using nonlocal games (in which two separated
parties must convince a referee that they have a valid coloringof the graph of a certain size). We show how these formulations
lead to quantum analogues of graph parameters, which canbe used in quantum information theory to study the powerof entanglement. We apply techniques from the theory of
noncommutative polynomial optimization to obtain hierarchiesof semidefinite programming bounds, unifying existing bounds.
Andreas Bluhm, Technical University of Munich (joint workwith Ion Nechita)
Compatibility of quantum measurements and inclusion
constants for free spectrahedra
One of the defining properties of quantum mechanics is theexistence of incompatible observables such as position and momentum. We will connect the problem of determining whether
a given set of measurements is compatible to the inclusion offree spectrahedra, which arise in convex optimization. We show
how results from algebraic convexity can be used to quantify
the degree of incompatibility of binary quantum measurements.
In particular, this new connection allows us to completely characterize the case in which the dimension of the quantum system
is exponential in the number of measurements.
Kun Fang, University of Cambridge (joint work with Hamza
Fawzi)Polynomial optimization on the sphere and entanglement
testing
We consider the problem of maximizing a homogeneouspolynomial over the unit sphere and its Sum-of-Square (SOS)
hierarchy relaxations. Exploiting the polynomial kernel technique, we show that the convergence rate of the hierarchy is noworse than O(d2 /l2 ), where d is the dimension of the sphereand l is the level of the hierarchy. Moreover, we explore theduality relation between the SOS polynomials and the quantum
extendable states in details, and further explain the connectionof our result with the one by Navascues, Owari & Plenio fromquantum information perspective.

CNV
Mon.1 H 1028
Convex-Composite Optimization (Part I)
Organizer: Tim Hoheisel
Chair: Tim Hoheisel
Andre Milzarek, The Chinese University of Hong Kong, Shenzhen (joint work with Michael Ulbrich)
A globalized semismooth Newton method for nonsmoothnonconvex optimization
We propose a globalized semismooth Newton frameworkfor solving convex-composite optimization problems involving
smooth nonconvex and nonsmooth convex terms in the objective function. A prox-type fixed point equation representing
the stationarity conditions forms the basis of the approach.
The proposed algorithm utilizes semismooth Newton steps
for the fixed point equation to accelerate an underlying globally convergent descent method. We present both global andlocal convergence results and provide numerical experiments
illustrating the efficiency of the semismooth Newton method.
James Burke, University of Washington (joint work with Abraham Engle)
Quadratic convergence of SQP-like methods for convexcomposite optimzation
We discuss the local convergence theory of Newton and quasiNewton methods for convex-composite optimization: minimize
f (x) = h(c(x)), where h is an infinite-valued closed proper
convex function and c is smooth. The focus is on the casewhere h is infinite-valued convex piecewise linear-quadratic.
The optimality conditions are embedded into a generalized
equation, and we show the strong metric subregularity andstrong metric regularity of the corresponding set-valued mappings. The classical convergence of Newton-type methods is
extended to this class of problems
Tim Hoheisel, McGill University, Montréal
On the Fenchel conjugate of convex-composite functions
We study conjugacy results of compositions of a convex function g and a nonlinear mapping F (possibly extended-valued ina generalized sense) such that the composition g ◦ F is still convex. This composite structure which preserves convexity models
a myriad of applications comprising e.g. conic programming,
convex additive composite minimization, variational Gram functions or vector optimization. Our methodology is largely basedon infimal convolution combined with cone-induced convexity.

43

CNV
Mon.1 H 1029
Recent Advances in First-Order Methods for Constrained
Optimization and Related Problems (Part I)
Organizers: Ion Necoara, Quoc Tran-Dinh
Chair: Quoc Tran-Dinh
Dirk Lorenz, TU Braunschweig
Extensions of the randomized Kaczmarz method: sparsity
and mismatched adjoints
The randomized Kaczmarz method is an incremental method
to solve linear equations. It uses only a single row of the systemin each step and hence, only needs very little storage. The standard Kaczmarz method approximates (in the underdeterminedcase) the minimum norm solution. In this talk we will study two
extensions of the method: First, we will show how to adapt themethod to produce sparse solutions of underdetermined andsecond, we investigate how a mismatch in the adjoint matrix
does affect the convergence.
Angelia Nedich, Arizona State University
A Smooth Inexact Penalty Reformulation of Convex Problems with Linear Constraints
We consider a constrained convex problem with linear inequalities and provide an inexact penalty reformulation. The
novelty is in the choice of the penalty functions, which aresmooth and can induce a non-zero penalty over some points infeasible region of the original problem. For problems with a large
number of constraints, a particular advantage of such a smoothpenalty-based reformulation is that it renders a penalized problem suitable for the implementation of fast incremental gradientmethods, which require only one sample from the inequality
constraints at each iteration (such as SAGA).
Ion Necoara, University Politehnica Bucharest (UPB)
Minibatch stochastic first order methods for convex optimization
We present convergence rates for minibatch stochastic firstorder methods with constant or variable stepsize for convex optimization problems, expressed in terms of expectation operator.
We show that these methods can achieve linear convergence
rate up to a constant proportional to stepsize and under somestrong stochastic bounded gradient condition even pure linear
convergence. Moreover, when variable stepsize is chosen we
derive sublinear convergence rates that show an explicit dependence on minibatch size. Applications of these results toconvex feasibility problems will be also discussed.

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DER
Mon.1 H 2038
Recent Advances in Derivative-Free Optimization (Part I)
Organizers: Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Chair: Francesco Rinaldi
Coralia Cartis, Oxford University (joint work with Adilet Otemissov)
Dimensionality reduction techniques for global optimization
We show that the scalability challenges of Global Optimisation (GO) algorithms can be overcome for functions withlow effective dimensionality, which are constant along certain
subspaces. We propose the use of random subspace embeddings within a(ny) global optimization algorithm, extending
Wang et al (2013). Using tools from random matrix theory andconic integral geometry, we investigate the success rates of our
low-dimensional embeddings of the original problem, in both astatic and adaptive formulation. We illustrate our algorithmic
proposals and theoretical findings numerically.
Jeffrey Larson, Argonne National Laboratory (joint work with
Kamil Khan, Matt Menickelly, Stefan Wild)
Manifold Sampling for Composite Nonconvex Nonsmooth
Optimization
We present and analyze a manifold sampling method forminimizing f (x) = h(F (x)) when h is nonsmooth, nonconvex,
and has a known structure and F is smooth, expensive toevaluate, and has a relatively unknown structure. Manifold
sampling seeks to take advantage of knowledge of h in order
to efficiently use past evaluations of F .
Florian Jarre, Heinrich-Heine Universität Düsseldorf (joint workwith Felix Lieder)
The use of second order cone programs in constrained
derivative-free optimization
We discuss a derivative-free solver for nonlinear programswith nonlinear equality and inequality constraints. The algorithm aims at finding a local minimizer by using finite differences and sequential quadratic programming (SQP). As
approximations of the derivatives are expensive compared to
the numerical computations that usually dominate the computational effort of NLP solvers a somewhat effortful trust-region
SQP-subproblem is solved at each iteration by second ordercone programs. The public domain implementation in Matlab
or Octave is suitable for small to medium size problems.

MUL
Mon.1 H 3013
Applications of Multi-Objective Optimization (Part I)
Organizers: Gabriele Eichfelder, Alexandra Schwartz
Chair: Alexandra Schwartz
Arun Pankajakshan, University College London (joint work with
Federico Galvanin)
A multi-objective optimal design of experiments framework for online model identification platforms
In many real world applications, sequential model-based
design of experiments (MBDoE) methods employed in online
model-based optimization platforms may involve more than
one objective in order to drive the real process towards optimal
targets, while guaranteeing the satisfaction of quality, cost andoperational constraints. In this work, a flexible multi-objective
optimal design of experiments framework suitable for online
modelling platforms is proposed. The proposed framework
is integrated in an online model identification platform forstudying the kinetics of chemical reaction systems.
Bennet Gebken, Paderborn University (joint work with Sebastian Peitz)
Inverse multiobjective optimization: Inferring decision criteria from data
It is a very challenging task to identify the objectives on
which a certain decision was based, in particular if several,
potentially conflicting criteria are equally important and acontinuous set of optimal compromise decisions exists. Thistask can be understood as the inverse problem of multiobjective
optimization, where the goal is to find the objective vectorof a given Pareto set. To this end, we present a method toconstruct the objective vector of a multiobjective optimization
problem (MOP) such that the Pareto critical set contains agiven set of data points.
Alexandra Schwartz, TU Darmstadt (joint work with Daniel
Nowak)A Generalized Nash Game for Computation Offloading
The number of tasks performed on wireless devices is steadily
increasing. Due to limited resources on these devices computation offloading became a relevant concept. In order to minimize
their own task completion time, users can offload parts of their
computation task to a cloudlet with limited power. We investigate the resulting nonconvex generalized Nash game, where thetime restriction from offloading is formulated as a vanishingconstraint. We show that a unique Nash exists and that theprice of anarchy is one, i.e. the equilibrium coincides with acentralized solution.

NON

45

NON

Mon.1 H 0105
Nonlinear and Stochastic Optimization (Part I)
Organizers: Albert Berahas, Geovani Grapiglia
Chair: Albert Berahas

Mon.1 H 2032
Smooth Optimization (Part I)
Organizers: Dominique Orban, Michael Saunders
Chair: Dominique Orban

Jorge Nocedal, Northwestern University (joint work with
Richard Byrd, Yuchen Xie)
Analysis of the BFGS Method with Errors

Elizabeth Wong, UC San Diego (joint work with Anders Forsgren, Philip Gill)
A method for degenerate convex quadratic programming

The classical convergence analysis of quasi-Newton methods
assumes that the function and gradients employed at each
iteration are exact. Here we consider the case when there are(bounded) errors in both computations and study the complex
interaction between the Hessian update and step computation.
We establish convergence guarantees for a line search BFGS
method.

We consider the formulation and analysis of an active-set
method for convex quadratic programming that is guaranteed
not to cycle. Numerical experiments are presented that illustrate the behavior of the method on some highly degenerate
quadratic programs.

Fred Roosta, University of Queensland (joint work with Yang
Liu, Michael Mahoney, Peng Xu)
Newton-MR: Newton’s Method Without Smoothness orConvexity
Establishing global convergence of the classical Newton’s
method has long been limited to making restrictive assumptions
on (strong) convexity as well as Lipschitz continuity of thegradient/Hessian. We show that two simple modifications of
the classical Newton’s method result in an algorithm, called
Newton-MR, which is almost indistinguishable from its classicalcounterpart but it can readily be applied to invex problems.
By introducing a weaker notion of joint regularity of Hessian
and gradient, we show that Newton-MR converges even in theabsence of the traditional smoothness assumptions.
Frank E. Curtis, Lehigh University
NonOpt: Nonsmooth Optimization Solver
We discuss NonOpt, an open source C++ solver for nonconvex, nonsmooth minimization problems. The code has methods based on both gradient sampling and bundle method approaches, all incorporating self-correcting BFGS inverse Hessian
approximations. The code comes equipped with its own specialized QP solver for solving the arising subproblems. It isalso extensible, intended to allow users to add their own step
computation strategies, line search routines, etc. Numericalexperiments show that the software is competitive with otheravailable codes.

David Ek, KTH Royal Institute of Technology (joint work withAnders Forsgren)
Newton-like approaches for approximately solving systems
of nonlinear equations arising in interior-point methods
We discuss Newton-like methods for approximately solving
systems of nonlinear equations. Equivalence is shown between a
Newton-like method with linearization of gradients to one withinverse Hessian approximations. We also discuss the relationship of these methods to other Newton-like methods as well as
their potential use in higher-order methods. Finally we specialize to systems of nonlinear equations arising in interior-point
methods.
Michael Saunders, Stanford University (joint work with Ding
Ma, Dominique Orban)
Experimental results with Algorithm NCL for constrained
optimization
We reimplement the LANCELOT augmented Lagrangian
method as a short sequence of nonlinearly constrained subproblems that can be solved efficiently by IPOPT and KNITRO,with warm starts on each subproblem. NCL succeeds on degenerate tax policy models that can’t be solved directly. Thesemodels (and algorithm NCL) are coded in AMPL. A Julia implementation of NCL gives results for standard test problems.

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NON
Mon.1 H 3006
Mixed-Integer Optimal Control and PDE Constrained Optimization (Part I)
Organizers: Falk Hante, Christian Kirches, Sven Leyffer
Chair: Sven Leyffer
Christian Clason, Universität Duisburg-Essen
Discrete material optimization for the wave equation
This talk is concerned with optimizing the spatially variable
wave speed in a scalar wave equation whose values are assumed
to be taken pointwise from a known finite set. Such a property
can be included in optimal control by a so-called ”multibang”
penalty that is nonsmooth but convex. Well-posedness as well as
the numerical solution of the regularized problem are discussed.

Richard Krug, Friedrich-Alexander-UniversitätErlangenNürnberg (joint work with Günter Leugering, Alexander Martin,
Martin Schmidt)
Domain decomposition of systems of hyperbolic equations
We develop a decomposition method in space and time for
optimal control problems on networks governed by systemsof hyperbolic PDEs. The optimality system of the optimal
control problem can be iteratively decomposed such that theresulting subsystems are defined on small time intervals and(possibly) single edges of the network graph. Then, virtual
control problems are introduced, which have optimality systems
that coincide with the decomposed subsystems. Finally, we
prove the convergence of this iterative method to the solutionof the original control problem.
Falk Hante, FAU Erlangen-Nürnberg (joint work with SimoneGöttlich, Andreas Potschka, Lars Schewe)
Penalty-approaches for partial minima in mixed-integer optimal control
We consider mixed-integer optimal control problems withcombinatorial constraints that couple over time such as minimum dwell times. A lifting allows a decomposition into amixed-integer optimal control problem without combinatorialconstraints and a mixed-integer problem for the combinatorialconstraints in the control space. The coupling is handled using
a penalty-approach. We present an exactness result for the
penalty which yields a solution approach that convergences topartial-epsilon-minima. The quality of these dedicated points
can be controlled using the penalty parameter.

PLA
Mon.1 H 3012
Optimization of Biological Systems (Part I)
Organizer: Pedro Gajardo
Chair: Pedro Gajardo
Anna Desilles, ENSTA ParisTech
HJB approach to optimal control problems with free initialconditions
This talk will fucus on some recent results about HJB approach to solve optimal control problems of Mayer’s type withfree initial state. First, wi show that such problems can besolved by using the associated value function as function of theinitial state. Then, some sensitivity relations can be establishedto link the subdifferential of the value function and the adjointstate of the problem, coming from the Pontryagin’s maximum
principle. These theoretic results will be illustrated by practicalexamples from optimal control of bio-processes.
Victor Riquelme, Universidad Tecnica Federico Santa Maria
Optimal control of diseases in jailed populations
We study the control of a communicable disease in a prison.
Its time evolution is modeled by a SIS model, typically used
for illnesses that do not confer immunity after recovery. To
control the disease, we consider an strategy that consists on
performing a screening to a proportion (control variable) of the
new prisoners, followed by a treatment. We determine the optimal strategies that minimize a tradeoff between the treatment
cost and the number of infective prisoners, in a predetermined
time horizon, and characterize qualitatively different behaviors
depending on the model parameters
Pedro Gajardo, Universidad Tecnica Federico Santa Maria
Links between sustainability standards, maximin, and viability: methods and applications
The maximin criterion, as the highest performance that canbe sustained over time, promotes intergenerational equity, apivotal issue for sustainability. The viable control approach, byinvestigating trajectories and actions complying over time withvarious standards and constraints, provides major insights into
strong sustainability. In this presentation we address the links
between maximin and viability approaches in a multi-criteria
context, showing practical methods for computing sustainability
standards based in dynamic programming principle and level-set
approach.

PDE
Mon.1 H 0106
Bilevel Optimization in Image Processing (Part I)
Organizers: Michael Hintermüller, Kostas Papafitsoros
Chair: Kostas Papafitsoros
Juan Carlos De los Reyes, Escuela Politécnica Nacional
Bilevel Optimal Parameters Selection in Nonlocal Image
Denoising
We propose a bilevel learning approach for the determinationof optimal weights in nonlocal image denoising. We consider
both spatial weights in front of the nonlocal regularizer, as wellas weights within the kernel of the nonlocal operator. In bothcases we investigate the differentiability of the solution operator
in function spaces and derive a first order optimality system thatcharacterizes local minima. For the numerical solution of the
problems, we propose a second- order optimization algorithm
in combination with a suitable finite element discretization of
the nonlocal denoising models.
Michael Hintermüller, Weierstrass Institute for Applied Analysis
and Stochastics
Several Applications of Bilevel Programming in Image Processing
Opportunities and challenges of bilevel optimization for applications in image processing are addressed. On the mathematicalside, analytical and algorithmic difficulties as well as solution
concepts are highlighted. Concerning imaging applications,
statistics based automated regularization function choice rules,
and blind deconvolution problems are in the focus.
Kostas Papafitsoros, Weierstrass Institute for Applied Analysis
and Stochastics
Generating structure non-smooth priors for image reconstruction
We will bind together and extend some recent developments
regarding data-driven non-smooth regularization techniques inimage processing through the means of bilevel minimization
schemes. The schemes, considered in function space, takeadvantage of dualization frameworks and they are designed to
produce spatially varying regularization parameters adapted tothe data for well-known regularizers, e.g. Total Variation andTotal Generalized Variation, leading to automated (monolithic),
image reconstruction workflows.

47

PDE
Mon.1 H 2013
Infinite-Dimensional Optimization of Nonlinear Systems(Part I)
Organizers: Fredi Tröltzsch, Irwin Yousept
Chair: Irwin Yousept
Fredi Tröltzsch, Technische Universität Berlin (joint work with
Eduardo Casas Renterı́a)
Sparse optimal control for a nonlinear parabolic equation
with mixed control-state constraints
A problem of sparse optimal control for a semilinear parabolic
equation is considered, where pointwise bounds on the control
and mixed pointwise control-state constraints are given. A
quadratic objective functional is to be minimized that includes
a Tikhonov regularization term and the L1 -norm of the control
that accounts for sparsity. Special emphasis is laid on existence and regularity of Lagrange multipliers associated withthe mixed control-state constraints. Since the interior of theunderlying non-negative cone is empty, the duality theory forlinear programming problems in Hilbert spaces is applied ina special way to prove the existence of Lagrange multipliers.
First-order necessary optimality conditions are established anddiscussed up to the sparsity of optimal controls.
Yifeng Xu, Shanghai Normal University (joint work with Irwin
Yousept, Jun Zou)
AFEM for quasilinear magnetostatic phenomena
In this talk, I introduce an adaptive edge element method fora quasilinear H(curl)-elliptic problem in magnetostatics, basedon a residual-type a posteriori error estimator and generalmarking strategies. The error estimator is shown to be reliable
and efficient, and its resulting sequence of adaptively generated
solutions converges strongly to the exact solution of the originalquasilinear system. Numerical experiments are provided to verify
the validity of the theoretical results.
Irwin Yousept, University of Duisburg-Essen
Hyperbolic Maxwell variational inequalities of the secondkindPhysical phenomena in electromagnetism can lead to hyperbolic variational inequalities with a Maxwell structure. They
include electromagnetic processes arising in polarizable media,
nonlinear Ohm’s law, and high-temperature superconductivity(HTS). In this talk, we present well-posedness and regularity
results for a class of hyperbolic Maxwell variational inequalitiesof the second kind, under different assumptions.

Mon.1 11:00–12:15

Parallel Sessions Abstracts Mon.1 11:00–12:15

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Parallel Sessions Abstracts Mon.1 11:00–12:15

ROB

ROB

Mon.1 H 1058
Advances in Robust Optimization (Part I)
Organizer: Shimrit Shtern
Chair: Shimrit ShternMon.1 H 3025
Advances in Optimization Under Uncertainty
Organizer: Fatma Kilinc-Karzan
Chair: Fatma Kilinc-Karzan

Krzysztof Postek, Erasmus University Rotterdam (joint workwith Shimrit Shtern)
First-order methods for solving robust optimization problems

Paul Grigas, University of California, Berkeley
New Results for Contextual Stochastic Optimization

We show how robust optimization problems where each
onstraints is convex in the decision variables and concave in theuncertain parameters, can be solved using a first-order saddlepoint algorithm. Importantly, the computations of proximal
operators involving the uncertain parameters (“pessimization”)
are parallelizable over the problem’s constraints. The scheme
has an O(1/T ) convergence rate with known both optimality
and feasibility guarantees.
Dick den Hertog, Tilburg University, The Netherlands
Solving hard non-robust optimization problems by Robust
Optimization
We discuss several hard classes of optimization problems
that contain no uncertainty, and that can be reformulatedto (adaptive) robust optimization (RO) problems. We startwith several geometrical problems that can be modeled as RO
problems. Then, we show that disjoint bilinear and concave
minimization problems with linear constraints can be efficiently
solved by reformulating these problems as adaptive linear RO
problems. This talk summarizes several (recent) papers and is
joint work with many other researchers. We expect that more
hard classes of problems can be treated in this way.
Shimrit Shtern, Technion-Israel Institute of Tecnology (joint
work with Dimitris Bertsimas, Bradley Sturt)
Data-Driven Two-Stage Linear Optimization: Feasibility
and Approximation Guarantees
In this talk, we focus on two-stage linear optimization problems wherein the information about uncertainty is given byhistorical data. We present a method that employs a simple robustification of the data combined with a multi-policy
approximation in order to solve this problem, and prove is combines (i) strong out-of-sample finite-sample guarantees and (ii)asymptotic optimality. A key contribution of this work is thedevelopment of novel geometric insights, which we use to show
our approximation is asymptotically optimal.

We consider a class of stochastic optimization problems
where the distribution defining the objective function can be
estimated based on a contextual feature vector. We propose
an estimation framework that leverages the structure of theunderlying optimization problem that will be solved given an
observed feature vector. We provide theoretical, algorithmic,
and computational results to demonstrate the validity andpracticality of our framework.
Anirudh Subramanyam, Argonne National Laboratory (joint
work with Kibaek Kim)
Wasserstein distributionally robust two-stage convex optimization with discrete uncertainty
We study two-stage distributionally robust convex programs
over Wasserstein balls centered at the empirical distribution.
Under this setting, recent works have studied single-stage convex programs and two-stage linear programs with continuous
parameters. We study two-stage convex conic programs where
the uncertain parameters are supported on mixed-integer programming representable sets. We show that these problems also
admit equivalent reformulations as finite convex programs andpropose an exact algorithm for their solution that is scalable
with respect to the size of the training dataset.
Prateek R Srivastava, University of Texas at Austin (joint workwith Grani A. Hanasusanto, Purnamrita Sarkar)
A Semidefinite Programming Based Kernel Clustering Algorithm for Gaussian Mixture Models with Outliers
We consider the problem of clustering data points generated
from a mixture of Gaussians in the presence of outliers. We
propose a semidefinite programming based algorithm that takes
the original data as input in the form of a Gaussian kernel
matrix, uses the SDP formulation to denoise the data andapplies spectral clustering to recover the true cluster labels.
We show that under a reasonable separation condition ouralgorithm is weakly consistent. We compare the performanceof our algorithm with existing algorithms like k-means, vanilla
spectral clustering and other SDP-based formulations.

SPA
Mon.1 H 1012
Recent Advances in First-Order Methods
Organizer: Shoham Sabach
Chair: Shoham Sabach
Russell Luke, Georg-August-University of Göttingen
Inconsistent Nonconvex Feasibility: algorithms and quantitative convergence results
Feasibility models can be found everywhere in optimizationand involve finding a point in the intersection of a collection(possibly infinite) of sets. The focus of most of the theory
for feasibility models is on the case where the intersection isnonempty. But in applications, one encounters infeasible problems more often than might be expected. We examine a few
high-profile instances of inconsistent feasibility and demonstrate
the application of a recently established theoretical frameworkfor proving convergence, with rates, of some elementary algorithms for inconsistent feasibility.
Yakov Vaisbourd, Tel Aviv University (joint work with Marc
Teboulle)
Novel Proximal Gradient Methods for Sparsity Constrained Nonnegative Matrix Factorization
In this talk we consider the nonnegative matrix factorization (NMF) problem with a particular focus on its sparsity
constrained variant (SNMF). Of the wide variety of methods
proposed for NMF, only few are applicable for SNMF and guarantee global convergence to a stationary point. We propose
several additional, globally convergent methods, which are applicable to SNMF. Some are extensions of existing state of
the art methods, while others are genuine proximal methods
that tackle non standard decompositions of NMF, leading to
subproblems lacking a Lipschitz continuous gradient objective.
Shoham Sabach, Technion-Israel Institute of Technology
Recent Advances in Proximal Methods for Composite Minimization
Proximal based methods are nowadays starring in optimization algorithms, and are effectively used in a wide spectrum of
applications. This talk will present some recent work on theimpact of the proximal framework for composite minimization,with a particular focus on convergence analysis and applications.

49

STO
Mon.1 H 0110
Stochastic Approximation and Reinforcement Learning(Part I)
Organizer: Alec Koppel
Chair: Alec Koppel
Sean Meyn, University of Florida (joint work with Andrey Bernstein, Yue Chen, Marcello Colombino, Emiliano Dall’Anese,
Prashant Mehta)
Quasi-Stochastic Approximation and Off-Policy Reinforcement Learning
The talk will survey new results in the area of “quasistochastic approximation” in which all of the processes underconsideration are deterministic, much like quasi-Monte-Carlofor variance reduction in simulation. Variance reduction canbe substantial, but only if the parameters in the algorithm
are chosen with attention to constraints. These results yield a
new class of reinforcement learning algorithms for deterministic
state space models. Specifically, we propose a class of algorithms for policy evaluation, using a different policy designed
to introduce “exploration”.
Huizhen Yu, University of Alberta (joint work with Rupam
Mahmood, Richard Sutton)
On Generalized Bellman Equations and TemporalDifference (TD) Learning for Policy Evaluation in MDPs
We introduce a new class of TD(lambda) algorithms forpolicy evaluation, based on the idea of randomized stopping
times and generalized Bellman equations. The key innovation isa dynamic scheme to judiciously set the lambda-parameters in
eligibility trace iterates, which is especially useful for balancing
the bias-variance tradeoff in off-policy learning. We prove weak
convergence and almost sure convergence results for several
gradient-type algorithms, including saddle-point and mirrordescent TD variants, by using ergodic theory for weak Feller
Markov chains and mean-ODE based proof methods.
Alec Koppel, U.S. Army Research Laboratory (joint work with
Tamer Başar, Kaiqing Zhang, Hao Zhu)
Global Convergence of Policy Gradient Methods: A Nonconvex Optimization Perspective
We propose a new variant for infinite-horizon problems thatuses a random rollout horizon to estimate the Q function.
Doing so yields unbiased gradient estimates, which yields analternative method to recover existing convergence results.
We then modify the proposed PG method by introducing aperiodically enlarged stepsize rule, which under the correlated
negative curvature condition (which holds when the reward
is strictly positive or negative), converges to a second-order
stationarity, i.e., approximate local maxima. Intriguingly, we
substantiate reward-reshaping from nonconvex optimization.

Mon.1 11:00–12:15

Parallel Sessions Abstracts Mon.1 11:00–12:15

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Mon.2 13:45–15:00
APP
Mon.2 13:45–15:00

Mon.2 H 0107
Optimization in Healthcare (Part II)
Organizers: Felix Jost, Christina Schenk
Chair: Christina Schenk
Corinna Maier, University of Potsdam (joint work with Niklas
Hartung, Wilhelm Huisinga, Charlotte Kloft, Jana de Wiljes)
Reinforcement Learning for Individualized Dosing Policies
in Oncology
For cytotoxic anticancer drugs, the combination of a narrow
therapeutic window and a high variability between patientscomplicates dosing policies, as strict adherence to standard
protocols may expose some patients to life-threatening toxicity
and others to ineffective treatment. Therefore, individualisationis required for safe and effective treatments. We present a reinforcement learning framework for individualised dosing policies
that not only takes into account prior knowledge from clinicalstudies, but also enables continued learning during ongoingtreatment.
Patrick-Marcel Lilienthal,
Otto-von-Guericke-Universität
Magdeburg (joint work with Sebastian Sager, ManuelTetschke)
Mathematical Modeling of Erythropoiesis and Treatment
Optimization in the Context of Polycythemia Vera
The disease Polycythemia Vera (PV) leads to patients, whichsuffer from an uncontrolled production of red blood cells (RBC).
If untreated, this might be fatal. Initial treatment is done byphlebotomy in certain regular intervals. Until now it is notknown, how to find an individual optimal timing of the treatment schedule, w.r.t. side effects. We extend our publishedmodel of RBC dynamics to PV and verify it using simulations.
Different approaches for the computation of optimal treatment schedules are formulated and discussed. We illustrate ourconclusions with numerical results
Felix Jost, Otto-von-Guericke University Magdeburg (joint workwith Hartmut Döhner, Thomas Fischer, Sebastian Sager, Enrico
Schalk, Daniela Weber)
Optimal Treatment Schedules for Acute Myeloid Leukemia
Patients During Consolidation Therapy
In this talk we present a pharmacokinetic-pharmacodynamic
model describing the dynamics of healthy white blood cells(WBCs) and leukemic cells during chemotherapy for acute
myeloid leukemia patients. Despite the primary treatment goalof killing cancer cells, a further clinically relevant event is thecytarabine-derived and lenograstim-reduced low number ofWBCs increasing the risk of infections and delayed or stopped
therapy. We present optimal treatment schedules derived by
optimization problems considering terms for disease progression,
healthiness and therapy costs.

BIG
Mon.2 H 0104
The Interface of Generalization and Optimization in Machine Learning (Part I)
Organizers: Benjamin Recht, Mahdi Soltanolkotabi
Chair: Mahdi Soltanolkotabi
Benjamin Recht, University of California, Berkeley
Training on the Test Set and Other Heresies
Conventional wisdom in machine learning taboos training on
the test set, interpolating the training data, and optimizing tohigh precision. This talk will present evidence demonstratingthat this conventional wisdom is wrong. I will additionally highlight commonly overlooked phenomena imperil the reliability ofcurrent learning systems: surprising sensitivity to how data is
generated and significant diminishing returns in model accuracies given increased compute resources. New best practices tomitigate these effects are critical for truly robust and reliablemachine learning.
Suriya Gunasekar, Toyota Technological Institute at Chicago
Characterizing optimization bias in terms of optimizationgeometry [canceled]
In the modern practice of machine learning, especially deeplearning, many successful models have far more trainable parameters compared to the number of training examples. Consequently, the optimization objective for such models havemultiple minimizers that perfectly fit the training data, but
most such minimizers will simply overfit or memorize the training data and will perform poorly on new examples. Whenminimizing the training objective for such ill posed problems,
the implicit inductive bias from optimization algorithms like(S)GD plays a crucial role in learning. In this talk, I will specifically focus on how the characterization of this optimization bias
depends on the update geometry of two families of local searchalgorithms – mirror descent w.r.t. strongly convex potentials
and steepest descent w.r.t general norms.
Tengyuan Liang, University of Chicago
New Thoughts on Adaptivity, Generalization and Interpolation
In the absence of explicit regularization, Neural Networks(NN) and Kernel Ridgeless Regression (KRR) have the potential to fit the training data perfectly. It has been observed
empirically, however, that such interpolated solutions can still
generalize well on test data. We show training NN with gradient flow learns an adaptive RKHS representation, and performs
the global least squares projection onto the adaptive RKHS,
simultaneously. We then isolate a phenomenon of implicit regularization for minimum-norm interpolated solutions for KRR
which could generalize well in the high dim setting.

Parallel Sessions Abstracts Mon.2 13:45–15:00

Yin Zhang, The Chinese University of Hong Kong, Shenzhen
Data clustering: a subspace-matching point of view andK-scalable algorithms
Data clustering is a fundamental unsupervised learning strategy. The most popular clustering method is arguably the Kmeans algorithm (K being the number of clusters), albeit it
is usually applied not directly to a dataset given, but to its
image in a processed “feature space” (such as the case in spectral clustering). The K-means algorithm, however, has been
observed to have a deteriorating performance as K increases.
In this talk, we will examine some clustering models froma subspace-matching viewpoint and promote a so-called Kindicators model. We apply a partial convex-relaxation scheme
to this model and construct an “essentially deterministic” algorithm requiring no randomized initialization. We will present
theoretical results to justify the proposed algorithm and giveextensive numerical results to show its superior scalability overK-means as the number of clusters grows.
Shuzhong Zhang, University of Minnesota, joint appointment
with Institute of Data and Decision Analytics, The Chinese
University of Hong Kong, Shenzhen (joint work with Bo Jiang,
Tianyi Lin)
Optimization Methods for Learning Models: Adaptation,
Acceleration, and Subsampling
In this talk we present a suite of algorithms for solving optimization models arising from applications in machine learningand statistics. Popular optimization methods for solving suchproblems include the high-order tensor approximation approach,which requires the knowledge on some problem parameters. To
make such methods practical, one will need to find ways of
implementation without such knowledge. We discuss methods
that exhibit an accelerated iteration bound while maintaining
the traits of being adaptive.
Bo Jiang, Shanghai University of Finance and Economics (joint
work with Haoyue Wang, Shuzhong Zhang)
An Optimal High-Order Tensor Method for Convex Optimization
Optimization algorithms using up to the d-th order derivative
information of the iterative solutions are called high-order tensor methods. In this talk, we propose a new high-order tensor
algorithm for the general composite case, with the iteration
complexity of O(1/k(3d+1)/2 ), which matches the lower boundfor the d-th order tensor methods, hence is optimal. Our approach is based on the so-called Accelerated Hybrid Proximal
Extragradient (A-HPE) framework.

VIS
Mon.2 H 3002
Linear Complementarity Problem (Part II)
Organizers: Tibor Illés, Janez Povh
Chair: Tibor Illés
Zsolt Darvay, Babes-Bolyai University (joint work with Tibor
Illés, Janez Povh, Petra Renáta Rigó)Predictor-corrector interior-point algorithm for sufficientlinear complementarity problems based on a new search
direction
We introduce a new predictor-corrector (PC) interior-point
algorithm (IPA), which is suitable for solving linear complementarity problem (LCP) with P*(K)-matrices. We use themethod of algebraically equivalent transformation (AET) of
the nonlinear equation of the system which defines the central
path. We show the complexity of this algorithm and implement
it in C++ programming language. We demonstrate its performance on two families of LCPs: LCP with matrices, whichhave positive handicap and on instances of LCP related to the
copositivity test.
Janez Povh, University of Ljubljana
Multi-type symmetric non-negative matrix factorization
Multi-type symmetric non-negative matrix factorization problem is a strong model to approach data fusion e.g. in bioinformatics. It can be formulated as a non-convex optimization
problem with the objective function being polynomial of degree
6 and the feasible set being defined by sign and complementarity constraints. In the talk we will present how to obtain
good solutions, often local extreme points, by the fixed point
method, the projected gradient method, the Alternating Direction Method of Multipliers and ADAM. We also demonstrate
the application of this approach in cancer research.

Mon.2 13:45–15:00

BIG
Mon.2 H 3004
Optimization Methods for Machine Learning and Data Science
Organizer: Shuzhong Zhang
Chair: Shuzhong Zhang

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VIS
Mon.2 13:45–15:00

Mon.2 H 3007
Nash Equilibrium Problems and Extensions (Part II)
Organizer: Anna Thünen
Chair: Anna Thünen
Mathias Staudigl, Maastricht University (joint work with Panayotis Mertikopoulos)
Stochastic Dual Averaging in Games and Variational Inequalities
We examine the convergence of a broad class of distributed
learning dynamics for games with continuous action sets. The
dynamics comprise a multi-agent generalization of Nesterov’s
dual averaging method, a primal-dual mirror descent methodthat has seen a major resurgence in large-scale optimization andmachine learning. To account for settings with high temporalvariability we adopt a continuous-time formulation of dual
averaging and we investigate the dynamics’ behavior whenplayers have noiseless or noisy information. Asymptotic andnon-asymptotic converegence results are presented.
Matt Barker, Imperial College London
A Comparison of Stationary Solutions to Mean Field
Games and the Best Reply Strategy

CON
Mon.2 H 0111
Conic Approaches to Infinite Dimensional Optimization(Part II)
Organizer: Juan Pablo Vielma
Chair: Juan Pablo Vielma
Lea Kapelevich, MIT Operations Research Center (joint workwith Chris Coey, Juan Pablo Vielma)
Sum-of-Squares Programming with New Cones
We discuss the modeling capabilities of our new conic interior
point solver, Hypatia.jl. We focus on problems that can benaturally formulated using nonsymmetric cones, such as theweighted sum of squares cone. The standard approach to solvethese problems involves utilizing semidefinite programmingreformulations, which unnecessarily increases the dimensionalityof the optimization problem. We show the advantages of being
able to optimize directly over known nonsymmetric cones andnewly defined cones.
Sascha Timme, Technische Universität Berlin (joint work withPaul Breiding)Solving systems of polynomials by homotopy continuation

In this talk I will briefly discuss the Best Reply Strategy (BRS)– an instantaneous response to a cost function – and how it canbe related to a Mean Field Game (MFG) through a short-time
rescaling. I will then focus on stationary solutions to linearquadratic MFGs and the BRS, describing a proof for existence
and uniqueness of such MFGs and comparing existence between
the two models. I will highlight some specific examples of MFGs
and BRS that show typical differences between the two models.
Finally, I will explain the importance of these differences andtheir modelling consequences.

Homotopy continuation methods allow to compute all isolated solutions of polynomial systems as well as representatives
for positive dimensional solution sets using techniques fromalgebraic geometry. After a short introduction to the fundamental constructions of the homotopy continuation approach, I willcontinue with describing an efficient approach to the solutionof polynomial systems with parametric coefficients by using
the monodromy action induced by the fundamental group of
the regular locus of the parameter space. This is demonstratedusing the Julia package HomotopyContinuation.jl.

Anna Thünen, Institut für Geometrie und Praktische Mathematik (IGPM), RWTH Aachen University (joint work withMichael Herty, Sonja Steffensen)
Multi-Level Mean Field Games

Tillmann Weisser, Los Alamos National Laboratory (joint workwith Carleton Coffrin)
GMP.jl: Modeling Conic Relaxations for the Generalized
Moment Problem

We discuss an application motivated two level game witha large amount of players. To overcome the challenge of highdimensionality due to the number of players, we derive themean field limit of infinitely many players. We discuss therelation between optimization and mean field limit for different
settings and establish conditions for consistency.

The generalized moment problem (GMP) is an infinite dimensional linear optimization problem on measures. Relaxations based on LP, SOCP, SDP and other cones are known toapproximate its solution. Since implementation of the different algorithms are spread over all platforms of mathematicalcomputing and not necessarily able to take into account newalgorithmic ideas or additional use of structure, it is difficult tocompare known and newly emerging approaches in practice andto decide which hierarchy works best for a particular problem.
With GMP.jl we provide a unified framework.

Parallel Sessions Abstracts Mon.2 13:45–15:00

Hamza Fawzi, University of Cambrige
On polyhedral approximations of the positive semidefinite
cone
Let D be the set of density matrices, i.e., the set of positive
definite matrices of unit trace. We show that any polytope P
that is sandwiched between (1 − )D and D (where the origin
is taken to be the scaled identity matrix) must have linear programming extension complexity at least exp(c sqrt(n)) wherec > 0 is a constant that depends only on . The main ingredient of our proof is hypercontractivity of the noise operator on
the hypercube.
Huikang Liu, The Chinese University of Hong Kong (joint workwith Rujun Jiang, Anthony Man-Cho So, Peng Wang)
Fast First-Order Methods for Quadratically Constrained
Quadratic Optimization Problems in Signal Processing
Large-scale quadratically constrained quadratic programming (QCQP) finds many applications in signal processing.
Traditional methods, like semidefinite relaxation (SDR) andsuccessive convex approximation (SCA), become inefficientbecause they are sensitive to the problem dimension. Thus,
we are motivated to design a fast fisrt-order method, called
linear programming-assisted subgradient decent (LPA-SD), totackle it. For LPA-SD, we only solve a dimension-free linear
programming in each iterate. Numerical results demonstrate its
potential in both computational efficiency and solution quality.
Anthony Man-Cho So, The Chinese University of Hong Kong
(joint work with K-F Cheung, Sherry Ni)
Mixed-Integer Conic Relaxation for TOA-Based Multiple
Source Localizaton
Source localization is among the most fundamental and studied problems in signal processing. In this talk, we consider the
problem of localizing multiple sources based on noisy distancemeasurements between the sources and the sensors, in which
the source-measurement association is unknown. We develop
a mixed-integer conic relaxation of the problem and propose
a second-order cone-based outer approximation algorithm forsolving it. We show that the proposed algorithm enjoys finite
convergence and present numerical results to demonstrate its
viability.

CNV
Mon.2 H 1028
Convex-Composite Optimization (Part II)
Organizer: Tim Hoheisel
Chair: Tim Hoheisel
Dmitry Kamzolov, Moscow Institute of Physics and Technology, MIPT (joint work with Pavel Dvurechensky, Alexander
Gasnikov)Composite High-Order Method for Convex Optimization
In this paper, we propose a new class of convex optimization
problem with the objective function formed as a sum of m + 1
functions with the different order of smoothness: non-smooth
but simple with known structure, with Lipschitz-continuous
gradient, with Lipschitz-continuous second-order derivative, ...,
with Lipschitz-continuous m-order derivative. To solve this problem, we propose a high-order optimization method, that takes
into account information about all functions. We prove theconvergence rate of this method. We obtain faster convergence
rates than the ones known in the literature.
Pedro Merino, Escuela Politécnica Nacional (joint work with
Juan Carlos De los Reyes)
A Second-order method with enriched hessian information
for composite sparse optimization problems
In this talk we present a second order method for solving
composite sparse optimization problems, which consist in minimizing the sum of a differentiable, possibly nonconvex function
and a nondifferentiable convex term. The composite nondifferentiable convex penalization is given by `1 -norm of a matrix
times the coefficient vector. Our proposed method generalizes
the previous OESOM algorithm in its three main ingredientsto the case of the generalized composite optimization: outhant directions, the projection step and, in particular, the fullsecond-order information.

Mon.2 13:45–15:00

CON
Mon.2 H 0112
Recent Advances and Applications of Conic Optimization
Organizer: Anthony Man-Cho So
Chair: Anthony Man-Cho So

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Mon.2 13:45–15:00

CNV
Mon.2 H 1029
Recent Advances in First-Order Methods for Constrained
Optimization and Related Problems (Part II)
Organizers: Shiqian Ma, Ion Necoara, Quoc Tran-Dinh,
Yangyang Xu
Chair: Ion Necoara

DER
Mon.2 H 2038
Challenging Applications in Derivative-Free Optimization(Part I)
Organizers: Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Chair: Margherita Porcelli

Nam Ho-Nguyen, (joint work with Fatma Kilinc-Karzan)
A dynamic primal-dual first-order framework for convex
optimization under uncertainty

Yves Lucet, University of British Columbia (joint work withMahdi Aziz, Warren Hare, Majid Jaberipour)
Multi-fidelity derivative-free algorithms for road design

Many optimization problems in practice must be solved under
uncertainty, i.e., only with noisy estimates of input parameters. We present a unified primal-dual framework for deriving
first-order algorithms for two paradigms of optimization under uncertainty: robust optimization (RO) where constraint
parameters are uncertain, and joint estimation-optimization(JEO) where objective parameters are uncertain. The key stepis to study a related ‘dynamic saddle point’ problem, and weshow how RO and JEO are covered by our framework throughchoosing the dynamic parameters in a particular manner.

We model the road design problem as a bilevel problem. The
top level calculates the road path from a satellite’s viewpoint
while the lower level is a mixed-integer linear problem (solving
the vertical alignment i.e. where to cut and fill the ground todesign the road and the earthwork i.e. what material to move tominimize costs) for which we build a variable-fidelity surrogate
using quantile regression. We compare two variable-fidelity
algorithms from the literature (a generalized pattern searchwith adaptive precision vs. a trust-region with controlled error)
and report significant speedups.

Guanghui Lan, Georgia Institute of Technology (joint work with
Digvijay Boob, Qi Deng)
Proximal point methods for constrained nonconvex optimization

Miguel Diago, Polytechnique Montréal (joint work with Peter
R. Armstrong, Nicolas Calvet)
Net power maximization in a beam-down solar concentrator

We present novel proximal-point methods for solving a classof nonconvex optimization problems with nonconvex constraints
and establish its computational complexity for both deterministic and stochastic problems. We will also illustrate the effectiveness of this algorithm when solving some application
problems in machine learning.

The reflectors of a beam-down solar concentrator are adjusted to maximize the net power collected at the receiver. The
performance of the solar plant is predicted with a Monte Carloray-tracing model as a function of a feasible reflector geometry.
Optimization is carried out with NOMAD, an instance of themesh-adaptive direct search (MADS) blackbox algorithm. Challenges include reducing the number of optimization variables,
dealing with the stochastic aspect of the blackbox, and theselection of effective MADS optimization parameters.

Quoc Tran-Dinh, University of North Carolina at Chapel Hill
Adaptive Primal-Dual Methods for Convex Optimization
with Faster Rates
We discuss a new class of primal-dual methods for solving
nonsmooth convex optimization problem ranging from unconstrained to constrained settings. The proposed methods achieve
optimal convergence rates and faster rates in the last iterates
instead of taking averaging sequence, and essentially maintain the same per-iteration complexity as in existing methods.
Our approach relies on two different frameworks: quadratic
penalty and augmented Lagrangian functions. In addition, it
combines with other techniques such as alternating, linearizing,
accelerated, and adaptive strategies in one

Sébastien Le Digabel, Polytechnique Montréal (joint work with
Dounia Lakhmiri, Christophe Tribes)
HYPERNOMAD: Hyper-parameter optimization of deepneural networks using mesh adaptive direct search
The performance of deep neural networks is highly sensitive
to the choice of the hyper-parameters that define the structure of the network and the learning process. Tuning a deepneural network is a tedious and time consuming process thatis often described as a “dark art”. This work introduces the
HYPERNOMAD package based on the MADS derivative-freealgorithm to solve this hyper-parameter optimization problem
including the handling of categorical variables. This approachis tested on the MNIST and CIFAR-10 datasets and achieves
results comparable to the current state of the art.

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55

Mon.2 H 0105
Nonlinear and Stochastic Optimization (Part II)
Organizers: Albert Berahas, Geovani Grapiglia
Chair: Albert Berahas

Floriane Mefo Kue, University of Siegen (joint work withThorsten Raasch)
Semismooth Newton methods in discrete TV and TGV
regularization

Alejandra Peña-Ordieres, Northwestern University, Evanston(joint work with James Luedtke, Andreas Wächter)
A Nonlinear Programming Reformulation of Chance Constraints

We consider the numerical minimization of Tikhonov-typefunctionals with discrete total generalized variation (TGV)
penalties. The minimization of the Tikhonov functional canbe interpreted as a bilevel optimization problem. The upper
level problem is unconstrained, and the lower level problem isa mixed `1 -TV separation problem. By exploiting the structureof the lower level problem, we can reformulate the first-order
optimality conditions as an equivalent piecewise smooth systemof equations which can then be treated by semismooth Newton
methods.

In this talk, we introduce a method for solving nonlinear
continuous optimization problems with chance constraints.
Our method is based on a reformulation of the probabilistic
constraint as a smooth sample-based quantile function. We
provide theoretical statistical guarantees of the approximation.
Furthermore, we propose an Sl1QP-type trust-region method
to solve instances with joint chance constraints. We show thatthe method scales well in the number of samples and that thesmoothing can be used to counteract the bias in the chanceconstraint approximation induced by the sample.

Zahra Forouzandeh, Faculdade de Engenharia da Universidade
do Porto (joint work with Mostafa Shamsi, Maria do Rosáriode Pinho)
Multi-objective optimal control problems: A new method
based on the Pascoletti-Serafini scalarization method

Stefan Wild, Argonne National Laboratory (joint work with
Matt Menickelly)
Manifold Sampling for Robust Calibration

A common approach to determine efficient solutions of aMulti-Objective Optimization Problems is to reformulate it asa parameter dependent Single-Objective Optimization: this is
the scalarization method. Here, we consider the well-known
Pascoletti-Serafini Scalarization approach to solve the nonconvex Multi-Objective Optimal Control Problems (MOOCPs). We
show that by restricting the parameter sets of the PascolettiSerafini Scalarization method, we overcome some of the difficulties. We illustrate the efficiency of the method for two
MOOCPs comprising both two and three different costs.
Alberto De Marchi, Bundeswehr University Munich (joint workwith Matthias Gerdts)
Mixed-Integer Linear-Quadratic Optimal Control with
Switching Costs
We discuss the linear-quadratic optimal control of continuoustime dynamical systems via continuous and discrete-valued
control variables, also considering state-independent switching costs. This is interpreted as a bilevel problem, involving
switching times optimization, for a given mode sequence, andcontinuous optimal control. Switching costs are formulated interms of cardinality, via the L0 “norm”. An efficient routine
for the simplex-constrained L0 proximal operator is devised
and adopted to solve some numerical examples through anaccelerated proximal gradient method.

NON

We adapt a manifold sampling algorithm for the composite
nonsmooth, nonconvex optimization formulations of model
calibration that arise when imposing robustness to outliers
present in training data. We demonstrate the approach on
objectives based on trimmed loss and highlight the challenges
resulting from the nonsmooth, nonconvex paradigm needed forrobust learning. Initial results demonstrate that the method
has favorable scaling properties. Savings in time on large-scale
problems arise at the expense of not certifying global optimality
in empirical studies, but the method also extends to cases where
the loss is determined by a black-box oracle.
Nikita Doikov, UCLouvain
Complexity of cubically regularized Newton method forminimizing uniformly convex functions
In this talk we discuss iteration complexity of cubicallyregularized proximal Newton method for solving composite
minimization problems with uniformly convex objective. We
introduce the notion of second-order condition number of acertain degree and present the linear rate of convergence in a
nondegenerate case. The algorithm automatically achieves thebest possible global complexity bound among different problem
classes of functions with Holder continuous Hessian of thesmooth part. This research was supported by ERC Advanced
Grant 788368.

Mon.2 13:45–15:00

MUL
Mon.2 H 3013
Applications of Multi-Objective Optimization (Part II)
Organizers: Gabriele Eichfelder, Alexandra Schwartz
Chair: Alberto De Marchi

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NON
Mon.2 13:45–15:00

Mon.2 H 2032
Smooth Optimization (Part II)
Organizers: Dominique Orban, Michael Saunders
Chair: Michael Saunders
Dominique Orban, GERAD & Polytechnique (joint work with
Ron Estrin, Michael Friedlander, Michael Saunders)
Implementing a smooth exact penalty function for constrained nonlinear optimization
We develop a general constrained optimization algorithm
based on a smooth penalty function proposed by Fletcher. The
main computational kernel is solving structured linear systems.
We show how to solve these systems efficiently by storing asingle factorization per iteration when the matrices are available
explicitly. We also give a factorization-free implementation.
The penalty function shows particular promise when such linear
systems can be solved efficiently, e.g., for PDE-constrained
optimization problems where efficient preconditioners exist.
Marie-Ange Dahito, PSA Group / École Polytechnique (joint
work with Dominique Orban)
The Conjugate Residual Method in Linesearch and TrustRegion Methods
Like the conjugate gradient method (CG), the conjugate
residual method (CR), proposed by Hestenes and Stiefel, hasproperties that are desirable in both linesearch and trust-region
contexts for optimization, but is only defined for symmetricpositive-definite operators. We investigate modifications thatmake CR suitable, even in the presence of negative curvature,and perform comparisons on convex and nonconvex problems
with CG. We give an extension suitable for nonlinear leastsquares problems. CR performs as well as or better than CG,
and yields savings in operator-vector products.
Douglas Gonçalves, UFSC Federal University of Santa Catarina(joint work with Fermin Bazan, Juliano Francisco, Lila Paredes)
Nonmonotone inexact restoration method for minimization with orthogonality constraints
We consider the problem of minimizing a differentiable functional restricted to the set of n × p matrices with orthonormal
columns. We present a nonmonotone line search variant of theinexact restoration method along with implementation details.
The restoration phase employs the Cayley transform, whichleads to an SVD-free scheme. By exploiting the ShermanMorrison-Woodburry formula this strategy proved to be quite
efficient when p is much smaller than n. Numerical experiments validate the reliability of our approach on a wide classof problems against a well established algorithm.

NON
Mon.2 H 2053
Variational Inequalities, Minimax Problems and GANs
(Part I)
Organizer: Konstantin Mishchenko
Chair: Konstantin Mishchenko
Simon Lacoste-Julien, University of Montreal & Mila & SAIL
Montreal
Negative Momentum for Improved Game Dynamics
Games generalize the single-objective optimization paradigm
by introducing different objective functions for different players. Differentiable games often proceed by simultaneous oralternating gradient updates. In machine learning, games are
gaining new importance through formulations like generative
adversarial networks (GANs) and actor-critic systems. However,compared to single-objective optimization, game dynamics ismore complex and less understood. In this work, we analyze
gradient-based methods with negative momentum on simple
games and show stability of alternating updates.
Dmitry Kovalev, King Abdullah University of Science and Technology (KAUST)
Revisiting Stochastic Extragradient Method.
We revisit stochastic extragradient methods for variational
inequalities. We propose a new scheme which uses the same
stochastic realization of the operator to perform an extragradient step. We prove convergence under the assumption thatvariance is bounded only at the optimal point and generalize
method to proximal setting. We improve state-of-the-art results
for bilinear problems and analyze convergence of the method
for non-convex minimization.
Gauthier Gidel, Université de Montréal
New Optimization Perspectives on Generative Adversarial
Networks
Generative adversarial networks (GANs) form a generativemodeling approach known for producing appealing samples,but they are notably difficult to train. One common way totackle this issue has been to propose new formulations of the
GAN objective. Yet, surprisingly few studies have looked at
optimization methods designed for this adversarial training. In
this work, we cast GAN optimization problems in the generalvariational inequality framework and investigate the effect of
the noise in this context.

Parallel Sessions Abstracts Mon.2 13:45–15:00

Sebastian Peitz, Paderborn University (joint work with Stefan
Klus)
Feedback control of nonlinear PDEs using KoopmanOperator-based switched systems
We present a framework for feedback control of PDEs using
Koopman operator-based reduced order models (K-ROMs).
The Koopman operator is a linear but infinite-dimensional
operator describing the dynamics of observables. By introducing
a finite number of constant controls, a dynamic control system
is transformed into a set of autonomous systems and thecorresponding optimal control problem into a switching time
optimization problem, which can be solved efficiently whenreplacing the PDE constraint of the autonomous systems by
K-ROMs. The approach is applied to examples of varying
complexity.
Mirko Hahn, Otto-von-Guericke University Magdeburg (joint
work with Sven Leyffer, Sebastian Sager)
Set-based Approaches for Mixed-Integer PDE Constrained
Optimization
We present a gradient descent method for optimal control
problems with ordinary or partial differential equations andinteger-valued distributed controls. Our method is based on aderivative concept similar to topological gradients. We formulate first-order necessary optimality conditions, briefly sketcha proof of approximate stationarity, and address issues withsecond derivatives and sufficient optimality conditions. We givenumerical results for two test problems based on the LotkaVolterra equations and the Laplace equation, respectively.
Paul Manns, TU Braunschweig (joint work with Christian
Kirches)
A continuous perspective on Mixed-Integer Optimal Control
PDE-constrained control problems with distributed integer
controls can be relaxed by means of partial outer convexification. Algorithms exist that allow to approximate optimal
relaxed controls with feasible integer controls. These algorithms
approximate relaxed controls in the weak∗ topology of L∞ andcan be interpreted as a constructive transfer of the Lyapunov
convexity theorem to mixed-integer optimal control, whichyields state vector approximation in norm for compact solution
operators of the underlying process. The chain of arguments isdemonstrated numerically.

PLA
Mon.2 H 3012
Optimization of Biological Systems (Part II)
Organizer: Pedro Gajardo
Chair: Pedro Gajardo
Kenza Boumaza, INRA (joint work with Nesrine Kalboussi,
Alain Rapaport, Roux Sébastien)
About optimal crop irrigation planing under water scarcity
We study theoretically on a simple crop model the problem
of optimizing the crop biomass at harvesting date, choosing
the temporal distribution of water under the constraint of a
total water volume available for the season. We show with thetools of the optimal control theory that the optimal solution
may present a singular arc, under a lower water availability,
which leads to a bang-singular-bang solution. This differs fromthe usual irrigation policies under water scarcity, which arebang-bang.
Ilya Dikariev, University BTU Cottbus-Senftenberg (joint workwith Ingrid M. Cholo, Gerard Olivar Tost)
Optimal public and private investments in a model of sustainable development
In this paper, we analyze the existence of an optimal resource
control problem with an infinite time horizon of a system ofdifferential equations with nonlinear dynamics and linear control.
Our system of differential equations corresponds to a modelof sustainable development. The variables in the state space
are related to economics, ecosystems and social dimensions.
We deal with the problem of non-convexity, and we prove theexistence of optimal solutions, which are interpreted in thesustainable and economic environment.

Mon.2 13:45–15:00

NON
Mon.2 H 3006
Mixed-Integer Optimal Control and PDE Constrained Optimization (Part II)
Organizers: Falk Hante, Christian Kirches, Sven Leyffer
Chair: Falk Hante

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PDE
Mon.2 13:45–15:00

Mon.2 H 0106
Bilevel Optimization in Image Processing (Part II)
Organizers: Michael Hintermüller, Kostas Papafitsoros
Chair: Michael Hintermüller
Elisa Davoli, University of Vienna (joint work with Irene Fonseca, Pan Liu)
Adaptive image processing: first order PDE constraint regularizers and a bilevel training scheme
In this talk we will introduce a novel class of regularizers,
providing a unified approach to standard regularizers such as
TV and TGV2 . By means of a bilevel training scheme we will
simultaneously identify optimal parameters and regularizers forany given class of training imaging data. Existence of solutions
to the training scheme will be shown via Gamma-convergence.
Finally, we will provide some explicit examples and numericalresults. This is joint work with Irene Fonseca (Carnegie Mellon
University) and Pan Liu (University of Cambridge).
Gernot Holler, University of Graz (joint work with Karl Kunisch)
Learning regularization operators using bilevel optimization
We address a learning approach for choosing regularization
operators from given families of operators for the regularizationof ill-posed inverse problems. Assuming that some groundtruth data is given, the basic idea is to learn regularization
operators by solving a bilevel optimization problem. Thereby,
the lower level problem, which is depending on the regularization
operators, is a Tikhonov-type regularized problem. As concrete
examples we consider families of operators arising in multipenalty Tikhonov regularization and a family of operators
inspired by nonlocal energy semi-norms.

PDE
Mon.2 H 2013
Infinite-Dimensional Optimization of Nonlinear Systems(Part II)
Organizers: Fredi Tröltzsch, Irwin Yousept
Chair: Fredi Tröltzsch
Antoine Laurain, Instituto de Matemática e Estatı́stica
Nonsmooth shape optimization and application to inverse
problems
The distributed expression of the shape derivative, also
known as volumetric form or weak form of the shape derivative,
allows to study shape calculus and optimization of nonsmoothgeometries such as Lipschitz domains and polygons. The distributed shape derivative has witnessed a surge of interest
recently, in particular due to new ideas for numerical applications. We will discuss some recent theoretical developments for
distributed shape derivatives, and their application to level set
methods in numerical shape optimization. We will then show
some applications to inverse problems.
De-Han Chen, Central China Normal University
Variational source condition for the reconstruction of distributed fluxes
This talk is devoted to the inverse problem of recovering the
unknown distributed flux on an inaccessible part of boundary
using measurement data on the accessible part. We establish and verify a variational source condition for this inverse
problem, leading to a logarithmic-type convergence rate forthe corresponding Tikhonov regularization method under alow Sobolev regularity assumption on the distributed flux. Our
proof is based on the conditional stability and Carleman estimates together with the complex interpolation theory on aproper Gelfand triplet

Jyrki Jauhiainen, University of Eastern Finland (joint work withPetri Kuusela, Aku Seppänen, Tuomo Valkonen)
An inexact relaxed Gauss–Newton applied to electrical
impedance tomography

Malte Winckler, University of Duisburg-Essen
Shape optimization subject to H(curl)-elliptic variational
inequalities in superconductivity

Partial differential equations (PDE)-based inverse problemssuch as image reconstruction in electrical impedance tomography (EIT)-often lead to non-convex optimization problems.
These problems are also non-smooth when total variation (TV)type regularization is employed. To improve upon current optimization methods, such as (semi-smooth) Newton’s method
and non-linear primal dual proximal splitting (NL-PDPS), we
propose a new alternative: an inexact relaxed Gauss-Newton
method. We investigate its convergence both theoretically andnumerically with experimental EIT studies.

We discuss a shape optimization problem subject to an
elliptic curl-curl VI of the second kind. By employing a MoreauYosida-type regularization to its dual formulation, we obtain thesufficient differentiablity property to apply the averaged adjoint
method. By means of this, we compute the shape derivative of
the regularized problem. After presenting a stability estimate
for the shape derivative w.r.t. the regularization parameter,
we prove the strong convergence of the optimal shapes andthe solutions. Finally, we apply the numerical algorithm to a
problem from the (HTS) superconductivity.

Parallel Sessions Abstracts Mon.2 13:45–15:00

ROB

Mon.2 H 2033
Optimal Control of Phase Field Models
Organizers: Carmen Gräßle, Tobias Keil
Chair: Carmen Gräßle

Mon.2 H 1058
New Advances in Robust Optimization
Organizer: Angelos Georghiou
Chair: Angelos Georghiou

Tobias Keil, WIAS Berlin
Optimal control of a coupled Cahn–Hilliard–Navier–Stokes
system with variable fluid densities

Han Yu, University of Southern California (joint work withAngelos Georghiou, Phebe Vayanos)
Robust Optimization with Decision-Dependent Information Discovery

This talk is concerned with the optimal control of two immiscible fluids. For the mathematical formulation we use a coupledCahn–Hilliard/Navier–Stokes system which involves a variational inequality of 4th order. We discuss the differentiability
properties of the control-to-state operator and the corresponding stationarity concepts for the control problem. We present
strong stationarity conditions and provide a numerical solution algorithm based on a bundle-free implicit programming
approach which terminates at an at least C-stationary point
which, in the best case, is even strongly stationary
Masoumeh Mohammadi, TU Darmstadt (joint work with Winnifried Wollner)
A priori error estimates for optimal control of a phase-field
fracture model
An optimal control problem for a time-discrete fracture propagation process is considered. The nonlinear fracture model istreated once as a linearized one, while the original nonlinear
model is dealt with afterwards. The discretization of the problem in both cases is done using a conforming finite element
method. Regarding the linearized case, a priori error estimates
for the control, state, and adjoint variables will be derived. The
discretized nonlinear fracture model will be analyzed as well,which leads to a quantitative error estimate, while we avoid
unrealistic regularity assumptions.

We consider two-stage robust optimization problems in which
the first stage variables decide on the uncertain parametersthat will be observed between the first and the second decision
stages. The information available in the second stage is thus
decision-dependent and can be discovered by making strategic
exploratory investments in the first stage. We propose a novel
min-max-min-max formulation of the problem. We prove thecorrectness of this formulation and leverage this new model
to provide a solution method inspired from the K-adaptability
approximation approach.
Viet Anh Nguyen, EPFL (joint work with Daniel Kuhn, Peyman
Mohajerin Esfahani)
Distributionally Robust Inverse Covariance Estimation:
The Wasserstein Shrinkage Estimator;
We introduce a distributionally robust maximum likelihood
estimation model with a Wasserstein ambiguity set to infer
the inverse covariance matrix of a p-dimensional Gaussian
random vector from n independent samples. We show that theestimation problem has an analytical solution that is interpreted
as a nonlinear shrinkage estimator. Besides being invertible
and well-conditioned, the new shrinkage estimator is rotationequivariant and preserves the order of the eigenvalues of thesample covariance matrix.

Henning Bonart, Technische Universität Berlin (joint work with
Christian Kahle, Jens-Uwe Repke)
Controlling Sliding Droplets with Optimal Contact Angle
Distributions

Angelos Georghiou, McGill University, Montréal (joint workwith Angelos Tsoukalas, Wolfram Wiesemann)
A Primal-Dual Lifting Scheme for Two-Stage Robust Optimization

In lab-on-a-chip devices liquid drops move over solid surfaces.
A specific contact angle distribution can be utilized to influenceboth speed and path of each drop. In previous works, we
improved and implemented a phase field model for energyconsistent simulations of moving contact lines. Building on
this, we present first results for the optimal control of droplets
on solid surfaces. The static contact angle distribution acts
as the control variable and the phase field model is used to
calculate the drop dynamics. As optimization problems, desired
drop shapes and positions are considered.

In this talk, we discuss convergent hierarchies of primal(conservative) and dual (progressive) bounds for two-stage
robust optimization problems that trade off the competing goalsof tractability and optimality: While the coarsest bounds recovera tractable but suboptimal affine decision rule of the two-stage
robust optimization problem, the refined bounds lift extreme
points of the uncertainty set until an exact but intractable
extreme point reformulation of the problem is obtained.

Mon.2 13:45–15:00

PDE

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ROB
Mon.2 13:45–15:00

Mon.2 H 3025
Advances in Robust Optimization (Part II)
Organizer: Krzysztof Postek
Chair: Krzysztof Postek
Ahmadreza Marandi, Eindhoven University of Technology(joint work with Geert-Jan van Houtum)
Robust location-transportation problems for integer lowdemand items
We analyze robust location-transportation problems withuncertain integer demand. The goal is to select locations of thewarehouses and determine their stock levels while considering
the optimal policy to satisfy the demand of customers. In thispresentation, we consider the integer transshipments between
customers and local warehouses. We provide theoretical andnumerical results on how to solve or approximate a multistage robust location-transportation problem. The numericalexperiments will show the efficiency of our methods.
Ernst Roos, Tilburg University (joint work with Ruud Brekelmans, Dick den Hertog)
Uncertainty Quantification for Mean-MAD Ambiguity
The Chebyshev inequality and its variants provide upperbounds on the tail probability of a random variable. While
it is tight, it has been criticized for only being attained bypathological distributions that abuse the unboundedness of the
support. We provide an alternative tight lower and upper boundon the tail probability given a bounded support, mean and mean
absolute deviation. This fundamental result allows us to findconvex reformulations of single and joint ambiguous chanceconstraints with right-hand side uncertainty and left-hand side
uncertainty with specific structural properties.
Stefan ten Eikelder, Tilburg University, The Netherlands (joint
work with Ali Ajdari, Thomas Bortfeld, Dick den Hertog)
Adjustable robust treatment-length optimization in radiation therapy
Technological advancements in radiation therapy (RT) allow
for the collection of biomarker data on individual patient response during treatment, and subsequent RT plan adaptations.
We present a mathematical framework that optimally adapts
the treatment-length of an RT plan, focusing on the inexactness of acquired biomarker data. We formulate the adaptive
treatment-length optimization problem as a 2-stage problem interms of Biological Effective Dose (BED). Using Adjustable Robust Optimization (ARO) techniques we derive explicit stage-2
decision rules and solve the optimization problem.

SPA
Mon.2 H 1012
Nonlinear Optimization Algorithms and Applications in
Data Analysis (Part I)
Organizers: Xin Liu, Cong Sun
Chair: Xin Liu
Yaxiang Yuan, State Key Laboratory of Scientific/Engineering
Computing, Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics andSystems Science, Chinese Academy of Sciences (joint workwith Xin Liu, Zaiwen Wen)
Efficient Optimization Algorithms For Large-scale Data
Analysis
This talk first reviews two efficient Newton type methods
based on problem structures: 1) semi-smooth Newton methods
for composite convex programs and its application to largescale semi-definite program problems and machine learning;2) an adaptive regularized Newton method for Riemannian
Optimization. Next, a few parallel optimization approaches
are discussed: 1) parallel subspace correction method for aclass of composite convex program; 2) parallelizable approach
for linear eigenvalue problems; 3) parallelizable approach for
optimization problems with orthogonality constraint.
Liping Wang, Nanjing University of Aeronautics and Astronautics
Robust ordinal regression induced by lp -centroid
We propose a novel type of class centroid derived from the
lp -norm (coined as lp -centroid) to overcome the drawbacks
above, and provide an optimization algorithm and corresponding convergence analysis for computing the lp -centroid. To
evaluate the effectiveness of lp -centroid in Ordinal regression(OR) context against noises, we then combine the lp -centroidwith two representative class-center-induced ORs. Finally, extensive OR experiments on synthetic and real-world datasets
demonstrate the effectiveness and superiority of the proposed
methods to related existing methods.
Cong Sun, Beijing University of Posts and Telecommunications(joint work with Jinpeng Liu, Yaxiang Yuan)
New gradient method with adaptive stepsize update strategy
In this work, a new stepsize update strategy for the gradientmethod are proposed. On one hand, the stepsizes are updated
in a cyclic way, where the Cauchy steps are combined with
fixed step lengths; on the other hand, the iteration number
in one cycle is adjusted adaptively according to the gradientresidue. Theorecially, the new method terminates in 7 iterations for 3 dimensional convex quadratic function minimization
problem; for general high dimensional problems, it converges
R-linearly. Numerical results show the superior performance ofthe proposed method over the states of the art.

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61

Stephen Tu, UC Berkeley (joint work with Benjamin Recht)
The Gap Between Model-Based and Model-Free Methods
on the Linear Quadratic Regulator: An Asymptotic Viewpoint
We study the sample complexity of popular model-based andmodel-free algorithms on the Linear Quadratic Regulator (LQR).
For policy evaluation, we show that a simple model-based plugin
method requires asymptotically less samples than least-squares
temporal difference (LSTD) to reach similar performance; thesample complexity gap between the two methods is at least afactor of state dimension. For simple problem instances, nominal(certainty equivalence principle) control requires several factorsof state and input dimension fewer samples than policy gradientmethod for similar performance.
Hoi-To Wai, CUHK (joint work with Belhal Karimi, Blażej
Miasojedow, Eric Moulines)
Non-asymptotic Rate for Biased Stochastic Approximation
SchemeIn this talk, we analyze a general stochastic approximation
scheme to minimize a non-convex, smooth objective function.
We consider an update procedure whose drift term depends
on a state-dependent Markov chain and the mean field is notnecessarily of gradient type, therefore it is a biased SA scheme.
Importantly, we provide the first non-asymptotic convergence
rate under such dynamical setting. We illustrate these settings
with the policy-gradient method for average reward maximization, highlighting its tradeoff in performance between the bias
and variance.

Tue.1 10:30–11:45

STO
Mon.2 H 0110
Stochastic Approximation and Reinforcement Learning(Part II)
Organizer: Alec Koppel
Chair: Alec Koppel

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Tue.1 10:30–11:45
APP
Tue.1 H 1012
Applications in Energy (Part I)
Chair: Bartosz Filipecki

Tue.1 10:30–11:45

Sauleh Siddiqui, Johns Hopkins University
Solving Problems with Equilibrium Constraints Applied toEnergy Markets
We provide a method to obtain stationary points for mathematical and equilibrium problems with equilibrium constraints(MPECs and EPECs). The MPECs and EPECs considered havelinear complementarity constraints at the lower level. Our approach is to express the MPEC and EPEC as a complementarity
problem by decomposing the dual variables associated with thebilinear constraints, and using a nonlinear optimization over apolytope to find stationary points. We apply this method to
the generic setting of energy markets that have operational
and infrastructure decisions among many players.
Paolo Pisciella, NTNU-The Norwegian University of Scienceand Technology
A CGE model for analyzing the economic impacts of theNorwegian energy transition
We introduce a multi-regional dynamic Computable General
Equilibrium model representing the Norwegian Economy witha particular focus on the energy system. The model is used
to study the effects of macroeconomic policies aimed at decarbonizing the Norwegian economy, such as the reduction of
the size of the oil industry, the introduction of carbon captureand storage in the gas industry as well as the introduction of ahydrogen industry and the transition of the transport sector to
the usage of hydrogen and electricity to substitute conventional
fossil fuels.
Bartosz Filipecki, TU Chemnitz (joint work with ChristophHelmberg)
Semidefinite programming approach to Optimal Power
Flow with FACTS devices
Producing accurate and secure solutions to the Optimal
Power Flow problem (OPF) becomes increasingly importantdue to rising demand and share of renewable energy sources.
To this end, we consider an OPF model with additional decisionvariables associated with FACTS devices, such as phase-shifting
transformers (PSTs) or thyristor-controlled series capacitors(TCSCs). We show, how a Lasserre hierarchy can be applied tothis model. Finally, we provide results of numerical experimentson the resulting semidefinite programming (SDP) relaxation.

BIG
Tue.1 H 2013
The Interface of Generalization and Optimization in Machine Learning (Part II)
Organizers: Benjamin Recht, Mahdi Soltanolkotabi
Chair: Benjamin Recht
Lorenzo Rosasco, Universita’ di Genova and MIT
Optimization for machine learning: from training to testerror
The focus on optimization is a major trend in modern machine learning. In turn, a number of optimization solutions havebeen recently developed and motivated by machine learning
applications. However, most optimization guarantees focus on
the training error, ignoring the performance at test time whichis the real goal in machine learning. In this talk, take steps tofill this gap in the context of least squares learning. We analyze
the learning (test) performance of accelerated and stochastic gradient methods. In particular, we discuss the influence
different learning assumptions.
Mahdi Soltanolkotabi, University of Southern California
Towards demystifying over-parameterization in deep learning
Many neural nets are trained in a regime where the parameters of the model exceed the size of the training dataset. Due totheir over-parameterized nature these models have the capacity
to (over)fit any set of labels including pure noise. Despite thishigh fitting capacity, somewhat paradoxically, models trained
via first-order methods continue to predict well on yet unseen
test data. In this talk I will discuss some results aimed atdemystifying such phenomena by demonstrating that gradientmethods achieve (1) global optima, (2) are robust to corruption,and (3) generalize to new test data.
Mikhail Belkin, Ohio State University
Interpolation and its implications
Classical statistics teaches the dangers of overfitting. Yet,in modern practice, deep networks that interpolate the data
(have near zero training loss) show excellent test performance.
In this talk I will show how classical and modern models can bereconciled within a single “double descent” risk curve, which extends the usual U-shaped bias-variance trade-off curve beyond
the point of interpolation. This understanding of model performance delineates the limits of classical analyses and opens newlines of inquiry into generalization and optimization propertiesof models.

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CON

Tue.1 H 3004
Optimization Methods for Graph Analytics
Organizers: Kimon Fountoulakis, Di Wang
Chair: Kimon Fountoulakis

Tue.1 H 0110
Hyperbolicity Cones and Spectrahedra (Part I)
Organizer: Markus Schweighofer
Chair: Markus Schweighofer

Nate Veldt, Cornell University
Metric-Constrained Linear Program Solvers for Graph
Clustering Relaxations

Daniel Plaumann, TU Dortmund University
Hyperbolicity cones and spectrahedra: Introduction

We present a new approach for solving a class of convex
relaxations for graph clustering objectives which involve triangleinequality constraints. Many approximation algorithms forgraph clustering rely on solving these metric-constrained optimization problems, but these are rarely implemented in practicedue to extremely high memory requirements. We present amemory-efficient approach for solving these problems basedon iterative projections methods. In practice we use our approach to address relaxations involving up to 2.9 trillion metricconstraints.
Rasmus Kyng, ETH ZürichLinear equations and optimization on graphs
Optimization on graphs is a powerful tool for understanding
networks, for example we can use it to plan flows of good or data,or to understand network clusters. Second order methods area powerful tool in optimization, but they require solving linear
equations, making them slow. However, second order methods
for graph optimization lead to structured linear equations, andwe can use this structure to solve the linear equations much
faster. We will see how to solve such linear equations, known
as Laplacians, quickly both in theory and in practice.
Kimon Fountoulakis, University of Waterloo
Local Graph Clustering
Large-scale graphs consist of interesting small or mediumscale clusters. Existing approaches require touching the wholegraph to find large clusters and they often fail to locate small
and medium-scale clusters. In this talk we will analyze theperformance of a local graph clustering approach, i.e., the `1 regularized PageRank model, on large-scale graphs with manysmall or medium-scale clusters. We will demonstrate averagecase results for the `1 -regularized PageRank model and we willshow that proximal gradient descent finds the optimal numberof non-zeros without touching the whole graph.

Spectrahedra are the domains of semidefinite programs,
contained in the a priori larger class of hyperbolicity regions
and hyperbolic programs. In recent years, a lot of research in
optimization and real algebraic geometry has been devoted tounderstanding the relation between these concepts. We will
give an overview, focussing on algebraic techniques and on
duality.
Simone Naldi, Université de Limoges (joint work with Mario
Kummer, Daniel Plaumann)Spectrahedral representations of plane hyperbolic curves
We describe a new method for constructing a spectrahedral
representation of the hyperbolicity region of a hyperbolic curvein the real projective plane. As a consequence, we show that
if the curve is smooth and defined over the rational numbers,
then there is a spectrahedral representation with rational matrices. This generalizes a classical construction for determinantal
representations of plane curves due to Dixon and relies on thespecial properties of real hyperbolic curves that interlace thegiven curve.
Mario Kummer, TU Berlin (joint work with Rainer Sinn)
When is the conic hull of a curve a hyperbolicity cone?
Let C be a semi-algebraic curve in n-dimensional space. We
will present obstructions and constructions for the conic hullof C to be a hyperbolicity cone. The tools we use are methods
from complex and real algebraic geometry.

Tue.1 10:30–11:45

BIG

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CON
Tue.1 H 2032
Methods for Sparse Polynomial Optimization
Organizers: Venkat Chandrasekaran, Riley Murray
Chair: Riley Murray
Tue.1 10:30–11:45

Henning Seidler, TU Berlin (joint work with Helena Müller,
Timo de Wolff)
Improved Lower Bounds for Global Polynomial Optimization
In this article, we describe a new way to obtain a candidate
for the global minimum of a multivariate polynomial, based on
its SONC decomposition. Furthermore, we present a branchand-bound algorithm to improve the lower bounds obtained
by SONC/SAGE. Applying this approach to thousands of test
cases, we mostly obtain small duality gaps. In particular, we
optimally solve the global minimization problem in about 75%of the investigated cases.
Venkat Chandrasekaran, California Institute of Technology(Caltech)
Generalized SAGE Certificates for Constrained Polynomial
Optimization
We describe a generalization of the SAGE polynomial cone
for obtaining bounds on constrained polynomial optimization
problems. Our approach leverages the fact that the SAGE
signomial cone conveniently and transparently blends withconvex duality. This result is extended to polynomials by thenotion of a “signomial representative.” Key properties of theoriginal SAGE polynomial cone (e.g. sparsity preservation) areretained by this more general approach. We illustrate the utilityof this methodology with a range of examples from the global
polynomial optimization literature.
Jie Wang, Peking University (joint work with Haokun Li, Bican
Xia)
Exploiting Term Sparsity in SOS Programming and Sparse
Polynomial Optimization
We consider a new pattern of sparsity for SOS programming
named cross sparsity patterns. A new sparse SOS algorithm is
proposed by exploiting cross sparsity patterns, and is applied tounconstrained polynomial optimization problems. It is provedthat the SOS decomposition obtained by the new algorithm
is always a refinement of the block-diagonalization obtainedby the sign-symmetry method. Various experiments show thatthe new algorithm dramatically saves the computational costcompared with existing tools and can handle some really hugepolynomials.

CON
Tue.1 H 3006
Theoretical Aspects of Conic Programming
Organizers: Mituhiro Fukuda, Masakazu Muramatsu, Makoto
Yamashita, Akiko Yoshise
Chair: Akiko Yoshise
Sunyoung Kim, Ewha Womans University (joint work withMasakazu Kojima, Kim-Chuan Toh)
Doubly nonnegative programs equivalent to completely
positive reformulations of quadratic optimization problems
with block clique structure
We exploit sparsity of the doubly nonnegative (DNN) relaxation and the completely positive (CPP) reformulation via the
CPP and DNN matrix completion. The equivalence between
the CPP and DNN relaxation is established under the assumption that the sparsity of the CPP relaxation is represented withblock clique graphs with the maximal clique size at most 4.
We also show that a block diagonal QOP with the maximal
clique of size 3 over the standard simplex can be reduced toa QOP whose optimal value can be exactly computed by the
DNN relaxation.
Natsuki Okamura, The University of Electro-Communications(joint work with Bruno Figueira Lourenço, Masakazu Muramatsu)
Solving SDP by projection and rescaling algorithms
Recently, Lourenco et al. proposed an extension of
Chubanov’s projection and rescaling algorithm for symmetric cone programming. Muramatsu et al. also proposed anoracle-based projection and rescaling algorithm for semi-infinite
programming. Both projection and rescaling algorithms cannaturally deal with semidefinite programming. We implemented
both algorithms in MATLAB and numerically compare them.
Makoto Yamashita, Tokyo Institute of Technology (joint workwith Mituhiro Fukuda, Sunyoung Kim, Takashi Nakagaki)
A dual spectral projected gradient method for logdeterminant semidefinite problems
In this talk, we discuss a spectral projected gradient (SPG)
method designed for the dual problem of a log-determinant
semidefinite problems (SDPs) with general linear constraints.
The feasible region of the dual problem is an intersection of abox-constrained set and a linear matrix inequality. In contrast
to a simple SPG that employs a single projection onto theintersection, the proposed method utilizes the structure of two
projections for the two sets. Numerical results on randomly
generated synthetic/deterministic data and gene expression
data show the efficacy of the proposed method.

Parallel Sessions Abstracts Tue.1 10:30–11:45

Boris Mordukhovich, Department of Mathematics, Wayne
State University (joint work with Ebrahim Sarabi)
Superlinear Convergence of SQP Methods in Conic Programming

CNV
Tue.1 H 1029
Recent Advances in First-Order Methods for Constrained
Optimization and Related Problems (Part III)
Organizers: Ion Necoara, Quoc Tran-Dinh
Chair: Quoc Tran-Dinh
Masaru Ito, Nihon University (joint work with Mituhiro Fukuda)
Nearly optimal first-order method under Hölderian error
bound: An adaptive proximal point approach

In this talk we present applications of second-order variational analysis and generalized differentiation to establish thesuperlinear convergence of SQP methods for general problems of conic programming. For the basic SQP method, theprimal-dual superlinear convergence is derived under an appropriate second-order condition and some stability/calmness
property, which automatically holds for nonlinear programs.
For the quasi-Newton SQP methods, the primal superlinear
convergence is obtained under the Dennis-More condition for
constrained optimization.

We develop a first-order method for convex composite optimization problem under the Hölderian Error Bound (HEB)
condition which is closely related to the Kurdyka-Lojasiewicz inequality and arises in many applications. The proposed method
does not require the prior knowledge on the HEB condition,
due to an adaptive proximal point approach. We prove thenear optimality of the proposed method in view of the iteration
complexity with respect to two kinds of approximation error:
the objective function value and the norm of the (generalized)
gradient.

Jingwei Liang, University of Cambridge (joint work with Clarice
Poon)
Geometry Based Acceleration for Non-smooth Optimisation

Vien Van Mai, KTH Royal Institue of Technology (joint workwith Mikael Johansson)
Anderson Acceleration for Constrained Convex Optimization

In this talk, we will present a geometry based adaptive acceleration framework for speeding up first-order methods whenapplied to non-smooth optimisation. Such a framework canrender algorithms which are able to automatically adjust themselves to the underlying geometry of the optimisation problems
and the structure of the first-order methods. State-of-the-art
performances are obtained on various non-smooth optimisation
problems and first-order methods, particularly the non-descenttype methods.

This paper introduces a novel technique for nonlinear acceleration of 1st-order methods for constrained convex optimization.
Previous studies of nonlinear acceleration have only been able
to provide convergence guarantees for unconstrained problems.
We focus on Anderson acceleration of the projected gradientdescent method, but our techniques can easily be extended
to more sophisticated algorithms, such as mirror descent. We
show that the convergence results for Anderson accelerationof smooth fixed-point iterations can be extended to the nonsmooth case under certain technical conditions.

Guoyin Li, University of New South Wales, Australia
Splitting methods for convex and nonconvex feasibility
problems
Splitting methods is a class of important and widely usedapproach for solving feasibility problems. The behaviour ofthese methods has been reasonably understood in the convex
setting. They have been further successfully applied to various
nonconvex instances recently; while the theoretical justificationin this latter setting is far from complete. We will examine global
convergence of several popular splitting methods (including
Douglas-Rachford and Peaceman Rachford splitting method)for general nonconvex nonsmooth optimization problems.

Tuomo Valkonen, Escuela Politécnica Nacional (joint work with
Christian Clason, Stanislav Mazurenko)
Primal-dual proximal splitting and generalized conjugationin non-smooth non-convex optimization
We demonstrate that difficult non-convex non-smooth optimization problems, including Nash equilibrium problems andPotts segmentation, can be written using generalized conjugates of convex functionals. We then show through detailed
convergence analysis that a conceptually straightforward extension of the primal-dual method of Chambolle and Pockis applicable to such problems. Under sufficient growth conditions we even demonstrate local linear convergence of themethod. We illustrate these theoretical results numerically on
the aforementioned example problems. (arXiv:1901.02746)

Tue.1 10:30–11:45

CNV
Tue.1 H 1028
Recent Advances of Nonsmooth Optimization
Organizer: Guoyin Li
Chair: Guoyin Li

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Tue.1 10:30–11:45

DER
Tue.1 H 2038
Derivative-Free Optimization Under Uncertainty (Part I)
Organizers: Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Chair: Stefan Wild

MUL
Tue.1 H 3013
Recent Developments in Set Optimization (Part I)
Organizers: Akhtar A. Khan, Elisabeth Köbis, Christiane Tammer
Chair: Akhtar A. Khan

Matt Menickelly, Argonne National Laboratory (joint work with
Stefan Wild)
Formulations and Methods for Derivative-Free Optimization Under Uncertainty

Niklas Hebestreit, Martin-Luther-University Halle-Wittenberg(joint work with Akhtar A. Khan, Elisabeth Köbis, Christiane
Tammer)Existence Theorems and Regularization Methods for Noncoercive Vector Variational and Vector Quasi-Variational
Inequalities

In this talk, we will discuss progress made in the incorporation of derivative-free techniques into various paradigms of
optimization under uncertainty. In particular, we will consider
a novel outer approximations method for derivative-free robust
optimization with particular application to so-called implementation error problems. We will also investigate the application ofa particular derivative-free method for nonsmooth optimization(manifold sampling) to robust data-fitting of least trimmed
estimators, as well to a feasibility restoration procedure forchance-constrained optimization.
Zaikun Zhang, Hong Kong Polytechnic University (joint workwith Serge Gratton, Luis Nunes Vicente)
Trust region methods based on inexact gradient information
We discuss the behavior of trust-region method assuming
the objective function is smooth yet the gradient information
available is inaccurate. We show trust-region method is quite
robust with respect to gradient inaccuracy. It converges even if
the gradient is evaluated with only one correct significant digitand encounters random failures with a positive probability. The
worst case complexity of the method is essentially the same as
when the gradient evaluation is accurate.
Morteza Kimiaei, University of Vienna (joint work with Arnold
Neumaier)
Efficient black box optimization with complexity guarantees
I’ll report on recent joint work with Morteza Kimiaei onderivative-free unconstrained optimization algorithms that bothhave high quality complexity guarantees for finding a local
minimizer and perform highly competitive with state-of-the-art
derivative-free solvers.

In this talk, we present a novel existence result for vector
variational inequalities. Since these problems are ill-posed ingeneral, we propose a regularization technique for non-coercive
problems which allows to derive existence statements evenwhen the common coercivity conditions in the literature do nothold. For this purpose, we replace our original problem by afamily of well-behaving problems and study their relationships.
Moreover, we apply our results to generalized vector variational
inequalities, multi-objective optimization problems and vector
quasi-variational inequalities.
Baasansuren Jadamba, Rochester Institute of Technology
Optimization Based Approaches for the Inverse Problemof Identifying Tumors
This talk focuses on the inverse problem of parameter identification in an incompressible linear elasticity system. Optimization formulations for the inverse problem, a stable identification
process using regularization, as well as optimality conditions,will be presented. Results of numerical simulations using synthetic data will be shown.
Ernest Quintana, Martin Luther University Halle-Wittenberg(joint work with Gemayqzel Bouza, Christiane Tammer, Anh
Tuan Vu)
Fermat’s Rule for Set Optimization with Set Relations
In this talk, we consider Set Optimization problems withrespect to the set approach. Specifically, we deal with thelower less and the upper less relations. Under convexity and
Lipschitz properties of the set valued objective map, we derivecorresponding properties for suitable scalarizing functionals.
We then obtain upper estimates for Clarke’s subdifferentialof these functionals. As a consequence of the scalarization
approach, we show a Fermat’s rule for this class of problems.
Ideas for a descent method are also discussed.

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NON

Tue.1 H 0105
Nonlinear and Stochastic Optimization (Part III)
Organizers: Albert Berahas, Geovani Grapiglia
Chair: Albert BerahasTue.1 H 2053
Nonconvex Optimization
Organizers: Asuman Ozdaglar, Nuri Vanli
Chair: Nuri Vanli

Clément Royer, University of Wisconsin-Madison (joint workwith Michael O’Neill, Stephen Wright)
Complexity guarantees for practical second-order algorithms

Adel Javanmard, University of Southern California
Analysis of a Two-Layer Neural Network via Displacement
Convexity

We discuss methods for smooth unconstrained optimization that converge to approximate second-order necessary
points with guaranteed complexity. These methods perform line
searches along two different types of directions, approximate
Newton directions and negative curvature directions for the
Hessian. They require evaluations of functions, gradients, andproducts of the Hessian with an arbitrary vector. The methods
have good practical performance on general problems as well
as best-in-class complexity guarantees.

We consider the problem of learning a concave function
defined on a compact set, using linear combinations of “bumplike” components. The resulting empirical risk minimization
problem is highly non-convex. We prove that, in the limit inwhich the number of neurons diverges, the evolution of gradientdescent converges to a Wasserstein gradient flow in the spaceof probability distributions. Also, the behavior of this gradient
flow can be described by a viscous porous medium equation.
Using Displacement Convexity property, we get exponential
dimension-free convergence rate for gradient descent.

Katya Scheinberg, Cornell University
New effective nonconvex approximations to expected risk
minimization

Murat Erdogdu, University of Toronto
Global Non-convex Optimization with Discretized Diffusions

In this talk we will present novel approach to empiricalrisk minimization. We will show that in the case of linear
predictors, the expected error and the expected ranking loss
can be effectively approximated by smooth functions whose
closed form expressions and those of their first (and second)order derivatives depend on the first and second moments ofthe data distribution, which can be precomputed. This results
in a surprisingly effective approach to linear classification. We
compare our method to the state-of-the-art approaches suchas logistic regression.

An Euler discretization of the Langevin diffusion is known toconverge to the global minimizers of certain convex and nonconvex optimization problems. We show that this property holds
for any suitably smooth diffusion and that different diffusions
are suitable for optimizing different classes of convex and nonconvex functions. This allows us to design diffusions suitablefor globally optimizing convex and non-convex functions notcovered by the existing Langevin theory. Our non-asymptotic
analysis delivers computable optimization and integration error
bounds.

Daniel Robinson, Lehigh University
Accelerated Methods for Problems with Group Sparsity

Nuri Vanli, Massachusetts Institute of Technology (joint workwith Murat Erdogdu, Asuman Ozdaglar, Pablo Parrilo)
Block-Coordinate Maximization Algorithm to Solve Large
SDPs with Burer-Monteiro Factorization

I discuss an optimization framework for solving problems
with sparsity inducing regularization. Such regularizers include
Lasso (L1 ), group Lasso, and latent group Lasso. The framework computes iterates by optimizing over small dimensional
subspaces, thus keeping the cost per iteration relatively low.
Theoretical convergence results and numerical tests on various
learning problems will be presented.

Semidefinite programming (SDP) with diagonal constraints
arise in many optimization and machine learning problems,such as Max-Cut, community detection and group synchronization. Although SDPs can be solved to arbitrary precisionin polynomial time, generic convex solvers do not scale wellwith the dimension of the problem. In order to address thisissue, Burer and Monteiro proposed to reduce the dimensionof the problem by appealing to a low-rank factorization, andsolve the subsequent non-convex problem instead. In this paper, we present coordinate ascent based methods to solve thisnon-convex problem with provable convergence guarantees. In
particular, we prove that the block-coordinate maximization
algorithm applied to the non-convex Burer-Monteiro approach
globally converges to a first-order stationary point with a sublinear rate without any assumptions on the problem. We also
show that the block-coordinate maximization algorithm is locally linearly convergent to a local maximum under local weakstrong concavity assumption. We establish that this assumption generically holds when the rank of the factorization is
sufficiently large. Furthermore, we propose an algorithm basedon the block-coordinate maximization and Lanczos methods
that is guaranteed to return a solution that provide 1 − O(1/r)
approximation to the original SDP, where r is the rank of
the factorization, and we quantify the number of iterations to
obtain such a solution. This approximation ratio is known tobe optimal under the unique games conjecture.

Tue.1 10:30–11:45

NON

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NON
Tue.1 H 3025
Advances in Nonlinear Optimization With Applications(Part I)
Organizers: Yu-Hong Dai, Deren Han
Chair: Yu-Hong Dai
Tue.1 10:30–11:45

Zhi-Long Dong, Xi’an Jiaotong University (joint work with
Yu-Hong Dai, Fengmin Xu)
Fast algorithms for large scale portfolio selection considering industries and investment styles
In this talk, we consider a large scale portfolio selection
problem with and without a sparsity constraint. Neutral constraints on industries are included as well as investment styles.
To develop fast algorithms for the use in the real financial
market, we shall expose the special structure of the problem,
whose Hessian is the summation of a diagonal matrix and a low
rank modification. Complexity of the algorithms are analyzed.
Extensive numerical experiments are conducted, which demonstrate the efficiency of the proposed algorithms compared withsome state-of-the-art solvers.
Xinxin Li, Jilin University (joint work with Ting Kei Pong, Hao
Sun, Henry Wolkowicz)
A Strictly Contractive Peaceman-Rachford Splitting
Method for the Doubly Nonnegative Relaxation of the
Minimum Cut Problem
The minimum cut problem, MC, consists in partitioning theset of nodes of a graph G into k subsets of given sizes in orderto minimize the number of edges cut after removing the k-th
set. Previous work on this topic uses eigenvalue, semidefinite
programming, SDP, and doubly non-negative bounds, DNN,
with the latter giving very good, but expensive, bounds. We
propose a method based on the strictly contractive PeacemanRachford splitting method, which can obtain the DNN boundefficiently when used in combination with regularization fromfacial reduction, FR. The FR allows for a natural splitting ofvariables in order to apply inexpensive constraints and include
redundant constraints. Numerical results on random data sets
and vertex separator problems show efficiency and robustnessof the proposed method.
Yu-Hong Dai, Chinese Academy of Sciences
Training GANs with Centripetal Acceleration
The training of generative adversarial networks suffers fromcyclic behaviours. In this paper, from the simple intuition thatthe direction of ventripetal acceletation of an object moving
in uniform circular motion is toward the center of the circle,
we present simultaneous and alternating centripetal acceleration methods to alleviate the cyclic behaviours. We conduct
nymerical simulations to test the effectivenes and efficiency
compared with several other methods.

PLA
Tue.1 H 3002
Optimization Applications in Mathematics of Planet Earth- Contributed Session
Chair: Maha H. Kaouri
Sophy Oliver, University of Oxford (joint work with Coralia
Cartis, Samar Khatiwala)
Ocean Biogeochemical Optimization in Earth System
Models
Ocean biogeochemical models used in climate change predictions are very computationally expensive and heavily parameterised. With derivatives too costly to compute, we optimise
the parameters within one such model using derivative-free algorithms with the aim of finding a good optimum in the fewest
possible function evaluations. We compare the performanceof the evolutionary algorithm CMA-ES which is a stochastic
global optimization method requiring more function evaluations, to the Py-BOBYQA and DFO-LS algorithms which arelocal derivative-free solvers requiring fewer evaluations.
Maha H. Kaouri, University of Reading (joint work with Coralia
Cartis, Amos S. Lawless, Nancy K. Nichols)
Globally convergent least-squares optimisation methods
for variational data assimilation
The variational data assimilation (VarDA) problem is usually
solved using a method equivalent to Gauss-Newton (GN) toobtain the initial conditions for a numerical weather forecast.
However, GN is not globally convergent and if poorly initialised,
may diverge such as when a long time window is used in
VarDA; a desirable feature that allows the use of more satellite
data. To overcome this, we apply two globally convergent
GN variants (line search & regularisation) to the long window
VarDA problem and show when they locate a more accurate
solution versus GN within the time and cost available.

Parallel Sessions Abstracts Tue.1 10:30–11:45

PDE

Tue.1 H 0106
Computational Design Optimization (Part I)
Organizers: Martin Siebenborn, Kathrin Welker
Chair: Kathrin Welker

Tue.1 H 0107
Optimal Control and Dynamical Systems (Part I)
Organizers: Cristopher Hermosilla, Michele Palladino
Chair: Cristopher Hermosilla

Eddie Wadbro, Umeå University (joint work with Bin Niu)
Multi-scale design of coated structures with periodic infill

Teresa Scarinci, University of Vienna
Primal-dual forward-backward methods
constrained optimization

We apply a multi-scale topology optimization method todesign an infill structure, which has a solid outer shell, or
coating, and a periodic infill pattern. Harmonic-mean based
non-linear filters realize length scale control on both the overallmacrostructure and the infill design on the microscale. The
design optimization of the infill lattice is performed simultaneously with the optimization of the macroscale structure, which
also includes the coating.

In this talk we investigate some direct methods to solve aclass of optimization problems in a Hilbert space framework. In
particular, we discuss some methods based on proximal splitting
techinques and propose some applications to PDE-constrained
optimization. We conclude the talk with a discussion aboutstochastic methods for problems with random data, numericalsimulations, and some future research directions. The talk isbased on a joint work with Caroline Geiersbach.

Johannes Haubner, (joint work with Michael Ulbrich)Numerical realization of shape optimization for unsteady
fluid-structure interaction

Karla Cortez, Faculdade de Engenharia da Universidade do
Porto (joint work with Maria do Rosário de Pinho)
Applications for Multiprocesses to Optimal path planningof AUVs under currents

We consider shape optimization for unsteady fluid-structureinteraction problems that couple the Navier–Stokes equations
with non-linear elasticity equations. We focus on the monolithic
approach in the ALE framework. Shape optimization by themethod of mappings approach yields an optimal control setting
and therefore can be used to drive an optimization algorithmwith adjoint based gradient computation. The continuous formulation of the problem and the numerical realization are
discussed. Numerical results for our implementation, whichbuilds on FEniCS, dolfin-adjoint and IPOPT are presented.
Kevin Sturm, TU Wien, Institut für Analysis and Scientific
Computing (joint work with Peter Gangl)
Shape and Topology Optimization subject to 3D Nonlinear
Magnetostatics – Part I: Sensitivity Analysis
We present shape and topological sensitivities for a 3D
nonlinear magnetostatic model of an electric motor. In order toderive the sensitivities, we use a Lagrangian approach, whichallows us to simplify the derivation under realistic physicalassumptions. The topological derivative for this quasilinear
problem involves the solution of two transmission problems on
the unbounded domain for each point of evaluation.

for

PDE-

We formulate the path planning of AUVs under currents
as a free end time multiprocess optimal control problem withstate constraints. Reformulating it as a fixed time problem,
we derive first order necessary conditions in the form of a
maximum principle. The notable feature of this result is that itcovers the case when the time spent in an intermediate system
is reduced to zero. It also provides us with the tools to designcodes to solve the problem numerically and extract informationon the optimal solution and its multipliers to partially validate
the numerical findings.
Dante Kalise, University of Nottingham
Robust optimal feedback control through high-dimensional
Hamilton-Jacobi-Isaacs equations
We propose a numerical scheme for the approximation ofhigh-dimensional, nonlinear Isaacs PDEs arising in robust optimal feedback control of nonlinear dynamics. The numericalmethod consists of a global polynomial ansatz together separability assumptions for the calculation of high-dimensional
integrals. The resulting Galerkin residual equation is solved bymeans of an alternating Newton-type/policy iteration methodfor differential games. We present numerical experiments illustrating the applicability of our approach in robust optimal
control of nonlinear parabolic PDEs.

Tue.1 10:30–11:45

PDE

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Tue.1 10:30–11:45

PDE
Tue.1 H 0112
Infinite-Dimensional Optimization of Nonlinear Systems(Part III)
Organizers: Fredi Tröltzsch, Irwin Yousept
Chair: Irwin Yousept

ROB
Tue.1 H 1058
Models and Algorithms for Dynamic Decision Making Under Uncertainty
Organizer: Man-Chung Yue
Chair: Man-Chung Yue

Matthias Heinkenschloss, Rice University
ADMM based preconditioners for time-dependent optimization problems

Kilian Schindler, EPFL (joint work with Daniel Kuhn, Napat
Rujeerapaiboon, Wolfram Wiesemann)
A Day-Ahead Decision Rule Method for Multi-Market
Multi-Reservoir Management

The Alternating Direction Method of Multipliers (ADMM)
can be applied to time-decomposition formulations of optimal
control problems to exploit parallelism. However, ADMM alone
converges slowly and the subproblem solves, while done inparallel, are relatively expensive. Instead ADMM is used as apreconditioner. This approach inherits parallelism, but reduces
the number of iterations and allows coarser subproblems solves.
Approach, theoretical and numerical results will be presented.
Moritz Ebeling-Rump, Weierstrass Institute for Applied Analysis and Stochastics (WIAS)
Topology Optimization subject to Additive Manufacturing
Constraints
In Topology Optimization the goal is to find the ideal material distribution in a domain subject to external forces. The
structure is optimal if it has the highest possible stiffness. A volume constraint ensures filigree structures, which are controlled
via a Ginzburg-Landau term. During 3D Printing overhangs
lead to instabilities, which have only been tackled unsatisfactorily. The novel idea is to incorporate an Additive Manufacturing
Constraint into the phase field method. Stability during 3DPrinting is assured, which solves a common additive manufacturing problem.
Dietmar Hömberg, Weierstrass Institute (joint work with
Manuel Arenas, Robert Lasarzik)
Optimal pre-heating strategies for the flame cutting ofsteel plates
We consider a mathematical model for the flame cuttingof steel. It is known that for high-strength steel grades thistechnology is prone to develop cracks at the cutting surface. To
mitigate this effect new strategies for inductive pre-heating havebeen investigated within the European Industrial Doctorate
project “MIMESIS – Mathematics and Materials Science for
Steel Production and Manufacturing”. In the talk we formulatea quasi-stationary optimal control problem for this problem,
derive the optimality system and discuss preliminary numericalresults.

Peak/off-peak spreads in European electricity spot markets
are eroding due to the nuclear phaseout and the recent growthin photovoltaic capacity. The reduced profitability of peak/offpeak arbitrage forces hydropower producers to participate in thereserve markets. We propose a two-layer stochastic programming framework for the optimal operation of a multi-reservoir
hydropower plant which sells energy on both the spot and thereserve markets. The backbone of this approach is a combination of decomposition and decision rule techniques. Numericalexperiments demonstrate its effectiveness.
Cagil Kocyigit, École polytechnique fédérale de Lausanne (joint
work with Daniel Kuhn, Napat Rujeerapaiboon)
A Two-Layer Multi-Armed Bandit Approach for Online
Multi-Item Pricing
The revenue-maximizing mechanism for selling I items to abuyer with additive and independent values is unknown, but
the better of separate item pricing and grand-bundle pricing
extracts a constant fraction of the optimal revenue. We study
an online version of this pricing problem, where the items are
sold to (each of) T identical and independent buyers arriving
sequentially. We propose a two-layer multi-armed bandit algorithm that is reminiscent of the UCB algorithm and offers an
asymptotic constant-factor approximation guarantee relative
to the (unknown) best offline mechanism.
Daniel Kuhn, EPFL (joint work with Peyman Mohajerin Esfahani, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh)
On the Connection between Chebyshev and Wasserstein
Ambiguity Sets in Distributionally Robust Optimization
Existing tractability results for distributionally robust optimization problems with Wasserstein ambiguity sets require
the nominal distribution to be discrete. In this talk we derive tractable conservative approximations for problems with
continuous nominal distributions by embedding Wassersteinambiguity sets into larger Chebyshev ambiguity sets. We identify interesting situations in which these approximations areexact.

Parallel Sessions Abstracts Tue.1 10:30–11:45

Bradley Sturt, Massachusetts Institute of Technology (joint
work with Dimitris Bertsimas, Shimrit Shtern)
A Data-Driven Approach for Multi-Stage Linear Optimization
We propose a novel data-driven framework for multi-stage
stochastic linear optimization where uncertainty is correlated
across stages. The proposed framework optimizes decision rulesby averaging over historical sample paths, and to avoid overfitting, each sample path is slightly perturbed by an adversary.
We show that this new framework is asymptotically optimal(even if uncertainty is arbitrarily correlated across stages) andcan be tractably approximated using techniques from robust
optimization. We also present new results and limitations ofpossible Wasserstein alternatives.
Omar El Housni, Columbia University (joint work with Vineet
Goyal)On the Optimality of Affine Policies for Budgeted Uncertainty Sets
We study the performance of affine policies for two-stage
adjustable robust optimization problem under budget of uncertainty sets. This problem is hard to approximate within afactor better than Ω(log n/ log log n) where n is the numberof decision variables. We show that surprisingly affine policies
provide the optimal approximation for this class of uncertainty
sets that matches the hardness of approximation; thereby, further confirming the power of affine policies. Our analysis also
gives a significantly faster algorithm to compute near-optimal
affine policies.
Rui Gao, University of Texas at Austin
Wasserstein distributionally robust optimization and statistical learning
In this talk, we consider a framework, called Wasserstein
distributionally robust optimization, that aims to find a solutionthat hedges against a set of distributions that are close to somenominal distribution in Wasserstein metric. We will discuss theconnection between this framework and regularization problems
in statistical learning, and study its generalization error bound.

SPA
Tue.1 H 3007
Methods and Theory for Nonconvex Optimization
Organizer: Nadav Hallak
Chair: Nadav HallakArmin Eftekhari, EPFL
Non-Convex Augmented Lagrangian Method for Learning
and Inference
We present efficient ALM and ADMM variants for solving
nonconvex optimization problems, with local sublinear convergence rates. We show how the ubiquitous Slater’s condition inconvex optimization gives its place to a geometric condition,
reminiscent of the Mangasarian-Fromovitz constraint qualification. We apply these new algorithms to a variety of applications,
including clustering, generalized eigenvalue decomposition, andeven robustness in deep nets. We will also show improved linear
rates can be obtained using the concepts of restricted strong
convexity and smoothness.
Peter Ochs, Saarland University
Model Function Based Conditional Gradient Method with
Armijo-like Line Search
The Conditional Gradient Method is generalized to a class of
non-smooth non-convex optimization problems with many applications in machine learning. The proposed algorithm iterates
by minimizing so-called model functions over the constraint
set. As special cases of the abstract framework, for example,
we develop an algorithm for additive composite problems andan algorithm for non-linear composite problems which leads
to a Gauss-Newton-type algorithm. Moreover, the framework
yields a hybrid version of Conditional Gradient and Proximal
Minimization schemes for free.
Nadav Hallak, Tel Aviv University (joint work with Marc
Teboulle)
Convergence to Second-Order Stationary Points: A Feasible Direction Approach
We focus on nonconvex problems minimizing a smooth nonconvex objective over a closed convex set. Finding second-order
stationary points for this class of problems is usually a hardtask. Relying on basic and fundamental feasible descent ideas,
this talk presents a surprisingly simple second order feasible
directions scheme for computing such points, with proven theoretical convergence guarantees (to second order stationarity)
under mild assumptions. (Numerical examples illustrate thepracticability of the proposed method for nonconvex problems.)

Tue.1 10:30–11:45

ROB
Tue.1 H 3012
Advances in Modeling Uncertainty via Budget-Constrained
Uncertainty Sets in Robust and Distributionally Robust
Optimization
Organizer: Karthyek Murthy
Chair: Karthyek Murthy

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STO
Tue.1 H 0104
Stochastic Approximation and Reinforcement Learning(Part III)
Organizer: Alec Koppel
Chair: Alec Koppel
Andrzej Ruszczynski, Rutgers University (joint work with Saeed
Ghadimi, Mengdi Wang)
A Single Time-scale Stochastic Approximation Method for
Nested Stochastic Optimization
Tue.2 13:15–14:30

We propose a new stochastic approximation algorithm tofind an approximate stationary point of a constrained nested
stochastic optimization. The algorithm has two auxiliary averaged sequences (filters) which estimate the gradient of thecomposite objective function and the inner function value. By
using a special Lyapunov function, we show the sample complexity of O(1/2 ) for finding an -approximate stationary point,
thus outperforming all extant methods for nested stochastic
approximation. We discuss applications to stochastic equilibria
and learning in dynamical systems
Vivak Patel, University of Wisconsin-Madison
Using Statistical Filters for Stochastic Optimization
Stochastic optimization methods fall into three generalparadigms: the Monte-Carlo (MC)/Sample Average Approximation (SAA) approach, the Bayesian Optimization (BO)approach, and the Stochastic Approximation (SA) approach.
While these paradigms enjoy strong theoretical support, thesemethods are often not always practical for well-documented
reasons. In this talk, we introduce a fourth paradigm for solving
such optimization problems using statistical filters.
Angeliki Kamoutsi, ETH Zürich (joint work with John Lygeros,
Tobias Sutter)
Randomized methods and PAC bounds for data-driven inverse stochastic optimal control
This work studies discrete-time Markov decision processes(MDPs) with continuous state and action spaces and addresses
the inverse problem of inferring a cost function from observed
optimal behavior. We propose a method that avoids solving
repeatedly the forward problem and at the same time provides
probabilistic performance guarantees on the quality of the recovered solution. Our approach is based on the LP formulationof MDPs, complementary slackness optimality conditions, recent developments in convex randomized optimization anduniform finite sample bounds from statistical learning theory.

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Tue.2 13:15–14:30
APP

Felipe Atenas, University of Chile (joint work with Claudia
Sagastizábal)
Planning Energy Investment under Uncertainty
We consider risk-averse stochastic programming models for
the Generation and Expansion Planning problem for energy systems with here-and-now investment decisions and generation
variables of recourse. The resulting problem is coupled bothalong scenarios and along power plants. To achieve decomposition, we combine the Progressive Hedging algorithm todeal with scenario separability, and an inexact proximal bundle
method to handle separability for different power plants in eachscenario subproblem. By suitably combining these approaches,a solution to the original problem is obtained.
Gopika Geetha Jayadev, University of Texas at Austin (joint
work with Erhan Kutanoglu, Benjamin Leibowicz)
U.S. Energy Infrastructure Of The Future: Electricity Capacity Planning Through 2050
We develop a large-scale linear programming framework that
optimizes long-term capacity investment decisions and operational schedules for energy supply and end-use technologies.
Our methodology extends an energy-economic modeling framework that simultaneously optimizes generation investments,
the locations of these investments as well as the underlying
transmission network linking generation facilities to different
demand regions. We study the evolution of technology mix
and transmission infrastructure for the US electricity market
over the span of 2016-2050.
Kristina Janzen, TU Darmstadt (joint work with Stefan Ulbrich)
Cost-optimal design of decentralized energy networks withcoupling of electricity, gas and heat networks including
renewable energies
The optimal design of energy networks is a decisive prerequisite for the realization of future decentralized supply structures
including renewable energy. The different energy carriers must
be planned simultaneously with regard to generation and loadconditions. Our aim is to minimize the costs of the various
acquisition opportunities, while satisfying the heat and power
demands. The design decisions and the detailed description of
the flow dynamics within each network result in a mixed integer nonconvex optimization problem. Furthermore numericalresults for different instances are presented.

BIG
Tue.2 H 2013
Optimization for Data Science (Part II)
Organizers: Ying Cui, Jong-Shi Pang
Chair: Jong-Shi Pang
Kim-Chuan Toh, National University of Singapore (joint workwith Xudong Li, Defeng Sun)An asymptotically superlinear convergent semismooth
Newton augmented Lagrangian method for LP
Powerful interior-point methods (IPM) based commercial
solvers have been hugely successful in solving large-scale linear
programming problems. Unfortunately, the natural remedy,
although can avoid the explicit computation of the coefficientmatrix and its factorization, are not practically viable due tothe inherent extreme ill-conditioning of the large scale normal
equation arising in each interior-point iteration. To provide a
better alternative choice for solving large scale LPs with dense
data, we propose a semismooth Newton based inexact proximal
augmented Lagrangian method.
Zhengling Qi, The George Washington University (joint workwith Yufeng Liu, Jong-Shi Pang)
Learning Optimal Individualized Decision Rules with Risk
ControlWith the emergence of precision medicine, estimation of optimal individualized decision rules (IDRs) has attracted tremendous attentions in many scientific areas. Motivated by complex
decision making procedures and the popular conditional value
at risk, we propose a robust criterion to evaluate IDRs to control the lower tails of each subject’s outcome. The resulting
optimal IDRs are robust in controlling adverse events. The
related nonconvex optimization algorithm will be discussed. Finally, I will present some optimization challenges about learning
optimal IDRs in the observational studies.
Meisam Razaviyayn, University of Southern California
First and Second Order Nash Equilibria of Non-Convex
Min-Max Games: Existence and Computation
Recent applications that arise in machine learning have
surged significant interest in solving min-max saddle point
games. While this problem has been extensively studied in
the convex-concave regime, our understanding of non-convex
case is very limited. In this talk, we discuss existence andcomputation of first and second order Nash Equilibria of suchgames in the non-convex regime. Then, we see applications ofour theory in training Generative Adversarial Networks.

Tue.2 13:15–14:30

Tue.2 H 1012
Applications in Energy (Part II)
Chair: Kristina Janzen

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BIG
Tue.2 H 3004
Recent Advances in Algorithms for Large-Scale Structured
Nonsmooth Optimization (Part I)
Organizer: Xudong Li
Chair: Xudong Li
Yangjing Zhang, National University of Singapore (joint workwith Defeng Sun, Kim-Chuan Toh, Ning Zhang)
An Efficient Linearly Convergent Regularized Proximal
Point Algorithm for Fused Multiple Graphical Lasso Problems
Tue.2 13:15–14:30

This paper focuses on the fused multiple graphical Lassomodel. For solving the model, we develop an efficient regularized proximal point algorithm, where the subproblem in each
iteration of the algorithm is solved by a superlinearly convergent semismooth Newton method. To implement the method,
we derive an explicit expression for the generalized Jacobianof the proximal mapping of the fused multiple graphical Lasso
regularizer. Our approach has exploited the underlying second order information. The efficiency and robustness of our
proposed algorithm are shown by numerical experiments.
Xin-Yee Lam, National University of Singapore (joint work withDefeng Sun, Kim-Chuan Toh)
A semi-proximal augmented Lagrangian based decomposition method for primal block angular convex composite
quadratic conic programming problems
We propose a semi-proximal augmented Lagrangian based
decomposition method for convex composite quadratic conic
programming problems with primal block angular structures.
Using our algorithmic framework, we are able to naturally derive
several well-known augmented Lagrangian based decomposition methods for stochastic programming such as the diagonal
quadratic approximation method of Mulvey and Ruszczynski.
We also propose a semi-proximal symmetric Gauss-Seidel basedalternating direction method of multipliers for solving the corresponding dual problem.
Lei Yang, National University of Singapore (joint work with
Jia Li, Defeng Sun, Kim-Chuan Toh)
A Fast Globally Linearly Convergent Algorithm for theComputation of Wasserstein Barycenters
In this talk, we consider the problem of computing a Wasserstein barycenter for a set of discrete distributions with finite
supports. When the supports in the barycenter are prespecified, this problem is essentially a large-scale linear programming(LP). To handle this LP, we derive its dual problem and adapt asymmetric Gauss-Seidel based ADMM. The global linear convergence rate is also given without any condition. All subproblems
involved can be solved exactly and efficiently. Numerical experiments show that our method is more efficient than two
existing representative methods and Gurobi.

CON
Tue.2 H 0110
Hyperbolicity Cones and Spectrahedra (Part II)
Organizer: Markus Schweighofer
Chair: Markus Schweighofer
Rainer Sinn, Freie Universität Berlin (joint work with Daniel
Plaumann, Stephan Weis)
Kippenhahn’s Theorem in higher dimensions
Kippenhahn’s Theorem relates the numerical range of acomplex square matrix to a hyperbolic plane curve via convex
duality. We will discuss this result and its generalization tohigher dimensions (which goes by the name of joint numerical
ranges of a sequence of hermitian matrices) from the point ofview of convex algebraic geometry.
Markus Schweighofer, Universität Konstanz
Spectrahedral relaxations of hyperbolicity cones
We present a new small spectrahedral relaxation of hyperbolicity cones that relies on the Helton-Vinnikov theorem andexhibit certain cases where it is exact.

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CON
Tue.2 H 2032
Signomial Optimization and Log-Log Convexity
Organizers: Venkat Chandrasekaran, Riley Murray
Chair: Venkat Chandrasekaran

CON
Tue.2 H 3006
Solving Saddle-Point Problems via First-Order Methods
Organizer: Robert Freund
Chair: Robert Freund

Berk Ozturk, Massachusetts Institute of Technology
Methods and applications for signomial programming inengineering design

Renbo Zhao, Massachusetts Institute of Technology
Optimal Algorithms for Stochastic Three-Composite
Convex-Concave Saddle Point Problems [canceled]

In this talk we motivate further research in signomial programming (SP) for the solution of conceptual engineering design
problems. We use examples from aerospace engineering, fromaircraft design to network design and control, to demonstrate
the modeling capabilities and solution quality of SPs, and identify areas for future work. We introduce GPkit, a toolkit thatprovides abstractions, modeling tools and solution methods
to use SPs to explore complex tradespaces. Additionally, we
present methods to solve SPs under uncertainty, called robust
SPs, to trade off risk and performance.

We develop stochastic first-order primal-dual algorithms tosolve stochastic three-composite convex-concave saddle point
problems. When the saddle function is non-strongly convex in
the primal variable, we design an algorithm based on the PDHG
framework, that achieves the state-of-the-art oracle complexity.
Using this algorithm as subroutine, when the saddle function
is strongly convex in the primal variable, we develop a novel
stochastic restart scheme, whose oracle complexity is strictly
better than any of the existing ones, even in the deterministic
case.

Riley Murray, Caltech (joint work with Venkat Chandrasekaran,
Adam Wierman)
Solving Signomial Programs with SAGE Certificates andPartial Dualization

Panayotis Mertikopoulos, CNRS
Going the extra (gradient) mile in saddle-point problems

We show how SAGE nonnegativity certificates are compatible
with a technique known as partial dualization, whereby computationally tractable constraints are incorporated into a dual
problem without being moved to the Lagrangian. We consider
the resulting “generalized SAGE cone” as it pertains to signomial programming (SP). Matters such as coordinate system
invariance, sparsity preservation, and error bounds are addressed.
Our provided implementation shows that this approach cansolve many SPs from the literature to global optimality, without
resorting to cutting planes, or branch-and-bound.
Akshay Agrawal, Stanford University (joint work with Stephen
Boyd, Steven Diamond)
Log-Log Convexity
We introduce log-log convex programs (LLCPs): optimization problems with positive variables that become convex whenthe variables, objective functions, and constraint functions are
replaced with their logs. LLCPs strictly generalize geometricprogramming, and include interesting problems involving nonnegative matrices, and integral inequalities. We discuss thebasic calculus for this class of functions, and show when a given
LLCP can be cast to a format that is acceptable by modern
conic solvers. Illustrative examples are provided with CVXPY’s
new “disciplined geometric programming” feature.

Novel theoretical inroads on saddle-point problems involve
moving beyond the convex-concave framework. Here we analyze
the behavior of mirror descent (MD) in a class of non-monotone
problems whose solutions coincide with those of a naturally
associated variational inequality – a property which we call“coherence.” We show that “vanilla” MD converges under astrict version of coherence (but not otherwise) and that thisdeficiency is mitigated by taking an “extra-gradient” step whichleads to convergence in all coherent problems. We validate ouranalysis by numerical experiments in GANs.
Yangyang Xu, Rensselaer Poly. Institute (joint work with
Yuyuan Ouyang)
Lower complexity bounds of first-order methods for bilinear saddle-point problems
There have been many works studying the upper complexity
bounds of first-order methods for solving the convex-concave
bilinear saddle-point problem (SPP). In this talk I will show
the opposite direction by deriving lower complexity bounds
for first-order methods for SPPs. For convex-concave SPP, I
show that the lower complexity
bound is O(1/) to obtain√an -solution, and is O(1/ ) under primal strong convexity.
These established bounds are tight in terms of the dependenceon , and they imply the optimality of several existing first-ordermethods.

Tue.2 13:15–14:30

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CNV
Tue.2 H 1028
Disjunctive Structures in Mathematical Programming
Organizer: Matúš Benko
Chair: Matúš Benko
Patrick Mehlitz, Brandenburgische Technische Universität
Cottbus-Senftenberg (joint work with Christian Kanzow, Daniel
Steck)
Switching-constrained mathematical programs: Applications, optimality conditions, and a relaxation method
Tue.2 13:15–14:30

Mathematical programs with switching constraints are characterized by restrictions requiring the product of two functions to be zero. Switching structures appear frequently inthe context of optimal control and can be used to reformulatesemi-continuity conditions on variables or logical or-constraints.
In this talk, these applications of switching-constrained optimization are highlighted. Moreover, first-and second-order
optimality conditions for this problem class will be investigated. Finally, a relaxation method is suggested and results ofcomputational experiments are presented.
Matúš Benko, Johannes Kepler University Linz
New verifiable sufficient conditions for metric subregularity of constraint systems with application to disjunctive
programs and pseudo-normality
In this talk, we present a directional version of pseudonormality for very general constraints. This condition implies
the prominent error bound property/MSCQ. Moreover, it isnaturally milder than its standard (non-directional) counterpart, in particular GMFCQ, as well as the directional FOSCMS.
We apply the obtained results to the disjunctive programs,
where pseudo-normality assumes a simplified form, resulting
in verifiable point-based sufficient conditions for this property
and hence also for MSCQ. In particular, we recover SOSCMS
and the Robinson’s result on polyhedral multifunctions.
Michal Červinka, Czech Academy of Sciences (joint work withMatúš Benko, Tim Hoheisel)
Ortho-disjunctive programs: constraint qualifications andstationarity conditions
We introduce a class of ortho-disjunctive programs (ODPs),
which includes MPCCs, MPVCs and several further recently
introduced examples of disjunctive programs. We present a
tailored directional version of quasi-normality introduced forgeneral constraint systems which implies error bound property/metric subregularity CQ. Thus, we effectively recover or
improve some previously established results. Additionally, we
introduce tailored OD-versions of stationarity concepts andstrong CQs (LICQ, MFCQ etc) and provide a unifying framework for further results on stationarity conditions for all ODPs.

CNV
Tue.2 H 1029
Recent Advances in First-Order Methods for Constrained
Optimization and Related Problems (Part IV)
Organizers: Ion Necoara, Quoc Tran-Dinh
Chair: Ion Necoara
Stephen Becker, University of Colorado Boulder (joint workwith Alireza Doostan, David Kozak, Luis Tenorio)
Stochastic Subspace DescentSome optimization settings, such as certain types of PDEoptimization, do not give easy access to the gradient of theobjective function. We analyze a simple 0th-order derivativefree stochastic method that computes the directional derivativein a stochastically chosen subspace, and also develop andanalyze a variance-reduced version. Under an error boundcondition we show convergence, and under strong convexity
we show almost-sure convergence of the variables. Numericalexperiments on problems from Gaussian Processes and PDE
shape-optimization show good results even on non-convex
problems.
Qihang Lin, University of Iowa (joint work with Mingrui Liu,
Hassan Rafique, Tianbao Yang)
Solving Weakly-Convex-Weakly-Concave Saddle-Point
Problems as Successive Strongly Monotone Variational Inequalities
In this paper, we consider first-order algorithms for solving
a class of non-convex non-concave min-max problems, whose
objective function is weakly convex (resp. weakly concave) in
the minimization (resp. maximization). It has many important
applications in machine learning such as training Generative
Adversarial Networks. Our method is an inexact proximal point
method which solves the weakly monotone variational inequality
corresponding to the min-max problem. Our algorithm finds
a nearly -stationary point of the min-max problem with aprovable iteration complexity.
Phan Tu Vuong, University of Vienna
The Boosted DC Algorithm for nonsmooth functions
The Boosted Difference of Convex functions Algorithm(BDCA) was recently proposed for minimizing smooth DC
functions. BDCA accelerates the convergence of the classical
DCA thanks to an additional line search step. The purpose ofthis paper is twofold: to show that this scheme can be generalized and successfully applied to certain types of nonsmooth
DC functions, and to show that there is complete freedom inthe choice of the trial step size for the line search. We provethat any limit point of BDCA sequence is a critical point of the
problem. The convergent rate is obtained under KL property.

Parallel Sessions Abstracts Tue.2 13:15–14:30

Clément Royer, University of Wisconsin-Madison (joint workwith Serge Gratton, Luis Nunes Vicente)
A decoupled first/second-order steps technique and its application to nonconvex derivative-free optimization
In this talk, we describe a technique that separately computes
first-and second-order steps, in order to better relate each step
to its corresponding stationarity criterion. Such an approach
can lead to larger steps and decreases in the objective, which
positively impacts both the complexity analysis and the practicalbehavior. Although its applicability is wider, we present ourconcept within a trust-region framework without derivatives.
This allows us to highlight interesting connections between our
decoupling paradigm and the criticality steps used to guarantee
convergence of such methods.
Francesco Rinaldi, Università di Padova (joint work with Andrea Cristofari)Derivative-free optimization for structured problems
In this talk, we describe a new approach for minimizing a
black-box function over a structured convex set. We analyze thetheoretical properties of the method. Furthermore, we report
some numerical results showing that the algorithm scales up
well when the dimensions of the problems get large.
Jan Feiling, Porsche AG (joint work with Christian Ebenbauer)
Multi-Variable Derivative-Free Optimization Based on
Non-Commutative Maps
A novel class of derivative-free optimization algorithms basedon non-commutative maps for multi-variable optimization problems is presented. The main idea is to utilize certain noncommutative maps in order to approximate the gradient of
the objective function. We show how these maps can be constructed by so-called generating functions incorporated witheither single- or two-point objective evaluation polices and how
periodic exploration sequences can be designed to approximate
gradients. Finally, we analyze the convergence of the proposedclass of algorithms and present benchmarking results.

GLO
Tue.2 H 3002
Convergence Analysis and Kurdyka-Lojasiewicz Property
Organizer: Ting Kei Pong
Chair: Ting Kei Pong
Peiran Yu, The Hong Kong Polytechnic University (joint workwith Guoyin Li, Ting Kei Pong)
Deducing Kurdyka-Lojasiewicz exponent via inf-projection
KL exponent plays an important role in inducing the local
convergence rates of many first-order methods. These methods
are applied to solve large-scale optimization problems encountered in fields like compressed sensing and machine learning.
However, KL exponent is hard to estimate explicitly and few
results are known. In this talk, we study when inf-projection
preserves KL exponent. This enables us to deduce the KL exponent of some SDP-representable functions and some nonconvex
models such as least squares with rank constraint.
Rujun Jiang, Fudan University (joint work with Duan Li)
Novel Reformulations and Efficient Algorithms for the Generalized Trust Region Subproblem
We present a new solution framework to solve the generalized
trust region subproblem (GTRS) of minimizing a quadratic objective over a quadratic constraint. More specifically, we derivea convex quadratic reformulation of minimizing the maximumof the two convex quadratic functions for the case under ourinvestigation. We develop algorithms corresponding to two
different line search rules. We prove for both algorithms theirglobal sublinear convergence rates. The first algorithm admits
a local linear convergence rate by estimating the KurdykaLojasiewicz exponent at any optimal solution.
Tran Nghia, Oakland University
Metric subregularity of the subdifferentials with applications to well-posedness and linear convergence
Metric subregularity of subdifferentials is an important property to study linear convergence of many first-order methods
for solving convex and nonconvex optimization problems. This
talk reviews recent achievements and provides some new developments in this direction. Moreover, we will show how metricsubregularity of subdifferentials could play significant roles
in investigating well-posedness of optimization problems via
second-order variational analysis. Applications to some structured optimization problems such as `1 , nuclear normed, Poisson
regularized problems will be discussed.

Tue.2 13:15–14:30

DER
Tue.2 H 2038
Recent Advances in Derivative-Free Optimization (Part II)
Organizers: Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Chair: Sébastien Le Digabel

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MUL
Tue.2 H 3013
Recent Developments in Set Optimization (Part II)
Organizers: Akhtar A. Khan, Elisabeth Köbis, Christiane Tammer
Chair: Elisabeth Köbis
Christiane Tammer, Martin-Luther-University of HalleWittenberg (joint work with Truong Q. Bao)
Subdifferentials and SNC property of scalarization functionals with uniform level sets and applications
Tue.2 13:15–14:30

In this talk, we are dealing with necessary conditions for minimal solutions of constrained and unconstrained optimization
problems with respect to general domination sets by using wellknown nonlinear scalarization functionals with uniform levelsets. The primary objective of this work is to establish revised
formulas for basic and singular subdifferentials of the nonlinear
scalarization functionals.The second objective is to propose a
new way to scalarize a set-valued optimization problem.
Tamaki Tanaka, Niigata University
Sublinear-like scalarization scheme for sets and its applications
This paper is concerned with a certain historical backgroundon set relations and scalarization methods for sets. Based on abasic idea of sublinear scalarization in vector optimization, we
introduce a scalarization scheme for sets in a real vector spacesuch that each scalarizing function has an order-monotone property for set relation and inherited properties on cone-convexity
and cone-continuity. Accordingly, we show a certain application
for this idea to establish some kinds of set-valued inequalities.
Moreover, we shall mention another application to fuzzy theory.

Marius Durea, Alexandru Ioan Cuza University of Iasi (joint
work with Radu Strugariu)
Barrier methods for optimization problems with convex
constraint
In this talk, we consider some barrier methods for vector optimization problems with geometric and generalized inequality
constraints. Firstly, we investigate some constraint qualification
conditions and we compare them to the corresponding ones inliterature, and then we introduce some barrier functions and we
prove several of their basic properties in fairly general situations.
Finally, we derive convergence results of the associated barrier
method, and to this aim we restrict ourselves to convex caseand finite dimensional setting.

NON
Tue.2 H 0104
Recent Advances in First-Order Methods (Part I)
Organizers: Guanghui Lan, Yuyuan Ouyang, Yi Zhou
Chair: Yuyuan Ouyang
Renato Monteiro, Georgia Institute of Technology
An Average Curvature Accelerated Composite Gradient
Method for Nonconvex Smooth Composite Optimization
Problems
This talk discusses an accelerated composite gradient (ACG)
variant for solving nonconvex smooth composite minimization
problems. As opposed to well-known ACG variants which are
based on either a known Lipschitz gradient constant or thesequence of maximum observed curvatures, the current one isbased on the sequence of average observed curvatures.
William Hager, University of Florida (joint work with Hongchao
Zhang)
Inexact alternating direction multiplier methods for separable convex optimization
Inexact alternating direction multiplier methods (ADMMs)
are developed for solving general separable convex optimization problems with a linear constraint and with an objectivethat is the sum of smooth and nonsmooth terms. The approach involves linearized subproblems, a back substitution
step, and either gradient or accelerated gradient techniques.
Global convergence is established and convergence rate bounds
are obtained for both ergodic and non-ergodic iterates.
Dmitry Grishchenko, Université Grenoble Alpes (joint workwith Franck Iutzeler, Jerome Malick)
Identification-based first-order algorithms for distributed
learning
In this talk, we present a first-order optimization algorithm
for distributed learning problems. We propose an efficient sparsification of proximal-gradient updates to lift the communication
bottleneck of exchanging huge size updates between mastermachine and workers machines. Using a sketch-and-projecttechnique with projections onto near-optimal low-dimensional
subspaces, we get a significant performance improvement interms of convergence with respect to the total size of communication.

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Tue.2 H 2053
Interplay of Linear Algebra and Optimization (Part I)
Organizers: Robert Luce, Yuji Nakatsukasa
Chair: Yuji Nakatsukasa

Michael Overton, New York University (joint work with Azam
Asl)
Behavior of L-BFGS on Nonsmooth Problems in Theory
and in PracticeHenry Wolkowicz, University of Waterloo
Completions for Special Classes of Matrices: Euclidean Distance, Low Rank, sparse, and Toeplitz

Tue.2 H 0105
Nonlinear and Stochastic Optimization (Part IV)
Organizers: Albert Berahas, Geovani Grapiglia
Chair: Albert Berahas

When applied to minimize nonsmooth functions, the “full”
BFGS method works remarkably well, typically converging linearly to Clarke stationary values, with no counterexamples tothis behavior known in the convex case, though its theoreticalbehavior is not yet well understood. In contrast, limited-memory
BFGS may converge to non-stationary values, even on a simple
convex function, but this seems to be rare. We summarize ourexperience with L-BFGS in both theory and practice.
Anton Rodomanov, UCLouvain
Greedy Quasi-Newton Method with Explicit Superlinear
Convergence
We propose a new quasi-Newton method for unconstrained
minimization of smooth functions. Our method is based on the
famous BFGS scheme but it uses a greedily selected coordinate
vector for updating the Hessian approximation at each iteration instead of the previous search direction. We prove thatthe proposed method has local superlinear convergence andestablish a precise bound for its rate. To our knowledge, thisresult is the first explicit non-asymptotic rate of superlinear
convergence for quasi-Newton methods. All our conclusions
are confirmed by numerical experiments.
Albert Berahas, Lehigh University (joint work with Frank E.
Curtis, Baoyu Zhou)
Limited-Memory BFGS with Displacement Aggregation
We present a displacement aggregation strategy for thecurvature pairs stored in a limited-memory BFGS method suchthat the resulting (inverse) Hessian approximations are equal
to those derived from a full-memory BFGS method. Using said
strategy, an algorithm employing the limited-memory method
can achieve the same convergence properties as when fullmemory Hessian approximations are employed. We illustrate
the performance of an LBFGS algorithm that employs theaggregation strategy.

We consider the matrix completion problem for special classesof matrices. This includes EDM, low rank, robust PCA, andToeplitz. We consider both the exact and noisy cases. We include theoretical results as well as efficient numerical techniques.
Our tools are semidefinite programming, facial reduction, andtrust region subproblems.
Dominik Garmatter, TU Chemnitz (joint work with Margherita
Porcelli, Francesco Rinaldi, Martin Stoll)
An Improved Penalty Algorithm for mixed integer and PDE
constrained optimization problems
We present a novel Improved Penalty Algorithm (IPA) designed for large-scale problems. The Algorithm utilizes theframework of an existing Exact Penalty Algorithm to correctly
increase the penalization throughout its iteration, while, in each
iteration, it tries to find an iterate that decreases the current
objective function value. Such a decrease can be achieved via
a problem-specific perturbation strategy. Based on a numerical
toy problem, we will compare the IPA to existing penalization
strategies, simple rounding schemes, and a Branch and Boundroutine of Cplex.
Robert Luce, Gurobi
Regularization of binary quadratic programming problems
We consider the solution of binary quadratic programming(BQP) problems in the standard branch-and-bound framework.
The idempotency of binary variables allows for regularizationof the objective function without altering the set of optimal
solutions. Such regularization can serve two purposes. First,
it can be applied to ensure the convexity of the continuous
relaxation, and secondly to strengthen the root relaxation of(convex) BQP problems. Computing such a regularization canbe achieved by the solution of an auxiliary SDP, for which wecompare different computational techniques.

Tue.2 13:15–14:30

Parallel Sessions Abstracts Tue.2 13:15–14:30

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IMP

Tue.2 H 3025
Advances in Interior Point Methods
Organizer: Coralia Cartis
Chair: Margherita Porcelli

Tue.2 H 0111
Computational Optimization in Julia
Organizer: Abel Soares Siqueira
Chair: Michael Garstka

Renke Kuhlmann, University of Wisconsin-Madison
Learning to Steer Nonlinear Interior-Point Methods

Maxim Demenkov, Institute of Control Sciences
From concave programming to polytope projection with
Julia

Tue.2 13:15–14:30

Interior-point methods handle nonlinear programs by sequentially solving barrier subprograms with a decreasing sequenceof barrier parameters. In practice, adaptive barrier updates
have been shown to lead to superior performance. In this talk
we interpret the adaptive barrier update as a reinforcement
learning task and train a deep Q-learning to solve it. Numerical results based on an implementation within the nonlinear
programming solver WORHP show that the agent successfully
learns to steer the barrier parameter and additionally improves
WORHP’s performance on the CUTEst test set.
Andre Tits, University of Maryland College Park (joint workwith M. Paul Laiu)
Constraint Reduction in Infeasible-Start Interior-Point for
Imbalanced Large Sparse Convex Quadratic Optimization
A framework for accommodating infeasible starts, given auser-supplied feasible-start “base” iteration for CQP, is proposed and analyzed. Requirements to be satisfied by the baseiteration allow for inexact computation of search directions.
The framework is applied to a recent feasible-start “constraintreduced” MPC algorithm, involving a small “working set” ofconstraints, updated at each iteration. In early numerical tests
with Matlab/CVX, a speedup of up to 10x over SDPT3/SeDuMi
was observed, on both randomly generated and data fitting
problems with many more constraints than variables.
Margherita Porcelli, University of Bologna (joint work with
Stefania Bellavia, Jacek Gondzio)
A new interior point approach for low-rank semidefinite
programs
We address the solution of semidefinite programming (SDP)
problems in which the primal variable X is expected to be
low-rank at optimality, a common situation in relaxations ofcombinatorial optimization (maximum cut) or in matrix completion. SDPs are solved efficiently using interior-point methods(IPMs), but such algorithms typically converge to a maximumrank solution. We propose a new IPM approach which works
with a low-rank X and gradually reveals the optimal (minimum)
rank. Preliminary results show that using alternating directions
improves the efficiency of the linear algebra.

Concave programming (CP) is a problem of maximizing
convex function over a convex set of constraints. If the set ispolyhedral, the solutions of this global optimization problem
lie on its vertices. We consider a largely forgotten branchof research in CP, originated from the work of Hoang Tuy
and based on the cone splitting techniques. The same typeof methods could be applied to characterize vertices of thewhole polyhedron and to compute its projection in the lowerdimensional subspace. We describe our Julia implementationof the projection method for polytopes.
Mattias Fält, Lund University (joint work with Pontus Giselsson)
QPDAS: Dual Active Set Solver for Mixed Constraint
Quadratic Programming
We present a method for solving the general mixed constrained quadratic programming problem using an active set
method on the dual problem. There are two main contributions;
we present a new way of handling the factorization in the thedual problem, and show how iterative refinement can be used
to handle the problems arising when the dual is not positive
definite. An open source implementation in Julia is available,
which can be used to solve problems using arbitrary-precision
floating-point numbers.
Michael Garstka, Oxford University
COSMO – A conic operator splitting method for large
convex conic problems
COSMO is an operator splitting algorithm for convex optimisation problems with quadratic objective function and conic
constraints. At each step the algorithm alternates between solving a quasi-definite linear system with a constant coefficientmatrix and a projection onto convex sets. The solver is able
to exploit chordal sparsity in the problem data and to detect
infeasible problems. The low per-iteration computational costmakes the method particularly efficient for large problems. Our
Julia implementation COSMO.jl is open-source, extensible andperforms well on a variety of problem classes.

Parallel Sessions Abstracts Tue.2 13:15–14:30

Volker Schulz, Trier University
On the volume formulation for the shape Hessian
The shape Hessian is usually formulated as a boundary operator, which is even symmetric in the Riemannian variant.
However, it might be advantageous to use its volume formulation for analytical and also numerical reasons. This has the
drawback of a severe lack of definiteness. This talk discusses anapproach to deal with the quite large null space of the volumic
shape Hessian within an overall shape optimization algorithm
utilizing second order information.
Kathrin Welker, Helmut-Schmidt-Universität Hamburg
Computational investigations of shape optimization problems constrained by variational inequalities of the first kindIn this talk, we consider shape optimization problems constrained by variational inequalities (VI) in shape spaces. These
problems are in particular highly challenging because of two
main reasons: First, one needs to operate in inherently nonlinear, non-convex and infinite-dimensional spaces. Second, one
cannot expect the existence of the shape derivative, which imply that the adjoint state cannot be introduced and, thus, theproblem cannot be solved directly without any regularization
techniques. We investigate computationally a VI constrained
shape optimization problem of the first kind.
Peter Gangl, Graz University of Technology (joint work with
Kevin Sturm)
Shape and Topology Optimization subject to 3D Nonlinear
Magnetostatics – Part II: Numerics
We present the application of design optimization algorithms
based on the shape and topological derivative for 3D nonlinear magnetostatics to the optimization of an electric motor.
The topological derivative for this quasilinear problem involves
the solution of two transmission problems on the unboundeddomain for each point of evaluation. We present a way toefficiently evaluate this quantity on the whole design domain.
Moreover, we present optimization results obtained by a levelset algorithm which is based on the topological derivative, as
well as shape optimization results.

PDE
Tue.2 H 0107
Recent Advances in PDE Constrained Optimization(Part I)
Organizer: Axel Kröner
Chair: Axel KrönerSimon Funke, Simula Research Laboratory (joint work with
Jørgen Dokken, Sebastian Mitusch)
Algorithmic differentation for shape derivatives with PDE
constraints
Shape-derivatives combined with the adjoint approach offer
an efficient approach for solving shape optimisation problems,
but their derivation is error-prone especially for complex PDEs.
In this talk, we present an algorithmic differentiation tool that
automatically computes shape derivatives by exploiting thesymbolic representation of variational problems in the finite
element framework FEniCS. We demonstrate that our approachcomputes first and second order shape derivatives for a wide
range of PDEs and functionals, approaches optimal performanceand works naturally in parallel.
Constantin Christof, Technical University of Munich (joint workwith Boris Vexler)
New Regularity Results for a Class of Parabolic OptimalControl Problems with Pointwise State Constraints
This talk is concerned with parabolic optimal control problems that involve pointwise state constraints. We show that, if
the bound in the state constraint satisfies a suitable compatibility condition, then optimal controls enjoy L∞ (L2 ), L2 (H 1 )and (with a suitable Banach space Y) BV(Y ∗ )-regularity. In
contrast to classical approaches, our analysis requires neithera Slater point nor additional control constraints nor assumptions on the spatial dimension nor smoothness of the objectivefunction.
Axel Kröner, Humboldt-Universität zu Berlin (joint work with
Maria Soledad Aronna, Frederic Bonnans)
Optimal control of a semilinear heat equation with bilinear control-state terms subject to state and control constraints
In this talk we consider an optimal control problem governedby a semilinear heat equation with bilinear control-state terms
and subject to control and state constraints. The state constraints are of integral-type, the integral being with respect tothe space variable. The control is multidimensional. The costfunctional is of a tracking-type and contains a linear term in
the control variables. We derive second order necessary andsufficient conditions relying on the Goh transformation, the concept of alternative costates, and quasi-radial critical directions.

Tue.2 13:15–14:30

PDE
Tue.2 H 0106
Computational Design Optimization (Part II)
Organizers: Martin Siebenborn, Kathrin Welker
Chair: Martin Siebenborn

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PDE

ROB

Tue.2 H 0112
Optimal Control and Dynamical Systems (Part II)
Organizers: Cristopher Hermosilla, Michele Palladino
Chair: Cristopher HermosillaTue.2 H 1058
Advances in Data-Driven and Robust Optimization
Organizer: Velibor Misic
Chair: Velibor Misic

Nathalie T. Khalil, Universidade do Porto Faculdade de Engenharia
Numerical solutions for regular state-constrained optimal
control problems via an indirect approach

Vishal Gupta, USC Marshall School of Business (joint workwith Nathan Kallus)
Data-Pooling in Stochastic Optimization

Tue.2 13:15–14:30

We present an indirect method to solve state-constrained
optimal control problems. The presented method is based on
the maximum principle in Gamkrelidze’s form. We focus on aclass of problems having a certain regularity condition, which
will ensure the continuity of the measure Lagrange multiplierassociated with the state constraint. This property of themultiplier will play a key role in solving the two-point boundary
value problem resulting from the maximum principle. Several
illustrative applications to time optimal control problems are
considered.
Luis Briceño-Arias, Universidad Tecnica Federico Santa Maria(joint work with Dante Kalise, Francisco Silva-Alvarez)
Mean Field Games with local couplings: numerical approaches
We address the numerical approximation of Mean Field
Games with local couplings. For power-like Hamiltonians, weconsider both unconstrained and constrained stationary systems
with density constraints in order to model hard congestion
effects. For finite difference discretizations of the Mean Field
Game system, we follow a variational approach. We prove thatthe aforementioned schemes can be obtained as the optimality
system of suitably defined optimization problems. Next, westudy and compare several efficiently convergent first-ordermethods.
Daria Ghilli, University of Padua
Inverse problem of crack identification by shape optimal
control
We study an inverse problem of crack identification formulated as an optimal control problem with a PDE constraint
describing the anti-plane equilibrium of an elastic body witha stress-free crack under the action of a traction force. The
optimal control problem is formulated by minimizing the L2 distance between the displacement and the observation andthen is solved by shape optimization techniques via a Lagrangian approach. An algorithm is proposed based on a gradient step procedure and several numerical experiments arecarried out to show its performance in diverse situations.

Applications often involve solving thousands of potentiallyunrelated stochastic optimization problems, each with limited
data. Intuition suggests decoupling and solving these problems
separately. We propose a novel data-pooling algorithm thatcombines problems and outperforms decoupling, even if data are
independent. Our method does not require strong distributional
assumptions and applies to constrained, possibly non-convex
problems. As the number of problems grows large, our method
learns the optimal amount to pool, even if the expected amountof data per problem is bounded and small.
Andrew Li, Carnegie Mellon University (joint work with Jackie
Baek, Vivek Farias, Deeksha Sinha)
Toward a Genomic Liquid Biopsy
The cost of DNA sequencing has recently fallen to the pointthat an affordable blood test for early-stage cancer is nearlyfeasible. What remains is a massive variable selection problem.
We propose an efficient algorithm, based on a decomposition at the gene level, that scales to full genomic sequences
across thousands of patients. We contrast our selected variables
against DNA panels from two recent, high-profile studies anddemonstrate that our own panels achieve significantly higher
sensitivities at the same cost, along with accurate discrimination between cancer types for the first time.
Velibor Misic, UCLA (joint work with Yi-Chun Chen)
Decision Forests: A Nonparametric Model for Irrational
ChoiceMost choice models assume that customers obey the weakrationality property, but increasing empirical evidence suggeststhat customers. We propose a new model, the decision forestmodel, which models customers via a distribution over trees.
We characterize its representational power, study the model
complexity needed to fit a data set and present a practical estimation algorithm based on randomization and linear optimization. We show using real transaction data that this approachyields improvements in predictive accuracy over mainstream
models when customers behave irrationally.

Parallel Sessions Abstracts Tue.2 13:15–14:30

Ihsan Yanikoglu, Ozyegin University
Robust Parameter Design and Optimization for Injection
Molding
The aim of this study is to propose an optimization methodology to determine the values of the key decision parametersof an injection molding machine such that the desired quality criteria are met in a shorter production cycle time. The
proposed robust parameter design and optimization approach
utilizes the Taguchi methodology to find critical parameters
for use in the optimization, and it utilizes robust optimization
to immunize the obtained solution against estimation errors.
The ‘optimal’ designs found by the robust parameter designand optimization approach are implemented in real-life.
Shubhechyya Ghosal, Imperial College Business School (joint
work with Wolfram Wiesemann)
The Distributionally Robust Chance Constrained Vehicle
Routing Problem
We study a variant of the capacitated vehicle routing problem(CVRP), where demands are modelled as a random vector with
ambiguous distribution. We require the delivery schedule to befeasible with a probability of at least 1 − , where  is the risk
tolerance of the decision maker. We argue that the emerging
distributionally robust CVRP can be solved efficiently withstandard branch-and-cut algorithms whenever the ambiguity
set satisfies a subadditivity condition. We derive efficient cut
generation schemes for some widely used moment ambiguity
sets, and obtain favourable numerical results.
Marek Petrik, University of New Hampshire (joint work with
Chin Pang Ho, Wolfram Wiesemann)
Robust Bellman Updates in Log-Linear Time
Robust Markov decision processes (RMDPs) are a promisingframework for reliable data-driven dynamic decision-making.
Solving even medium-sized problems, however, can be quite
challenging. Rectangular RMDPs can be solved in polynomial time using linear programming but the computational
complexity is cubic in the number of states. This makes it computationally prohibitive to solve problems of even moderate
size. We describe new methods that can compute Bellman
updates in quasi-linear time for common types of rectangularambiguity sets using novel bisection and homotopy techniques.

SPA
Tue.2 H 3007
Nonlinear Optimization Algorithms and Applications in
Data Analysis (Part II)
Organizers: Xin Liu, Cong Sun
Chair: Cong Sun
Xin Liu, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Multipliers Correction Methods for Optimization Problems
with Orthogonality Constraints
We establish an algorithm framework for solving optimization
problems with orthogonality constraints. The new framework
combines a function value reduction step with a multipliercorrection step. The multiplier correction step minimizes theobjective function in the range space of the current iterate.
We also propose three algorithms which are called gradientreflection (GR), gradient projection (GP) and columnwise block
coordinate descent (CBCD), respectively. Preliminary numericalexperiments demonstrate that our new framework is of great
potential.
Xiaoyu Wang, Academy of Mathematics and Systems Science,
Chinese Academy of Sciences (joint work with Yaxiang Yuan)
Stochastic Trust Region Methods with Trust Region Radius Depending on Probabilistic Models
We present a stochastic trust region scheme in which thetrust region radius is directly related to the probabilistic models.
Especially, we show a specific algorithm STRME in which thetrust region radius is selected as depending linearly on model
gradient. Moreover, the complexity of STRME in nonconvex,
convex and strongly convex settings has all been analyzed.
Finally, some numerical experiments are carried out to reveal
the benefits of the proposed methods compared to the existing
stochastic trust region methods and other relevant stochastic
gradient methods.
Yanfei Wang, Chinese Academy of Sciences
Regularization and optimization methods for micro pore
structure analysis of shale based on neural networks withdeep learning
With the CT image segmentation, the images of shale micropores can be obtained, especially the pore type, shape, size,
spatial distribution and connectivity. In this work, based on
the reconstructed synchrotron radiation X-ray shale CT data,
we develop a neural network image segmentation techniquewith deep learning based on multi-energy CT image data inorder to obtain the 3D structural characteristics and spatial
distribution of shale. Since it is an ill-posed inverse problem,
regularization and optimization issues are discussed.

Tue.2 13:15–14:30

ROB
Tue.2 H 3012
Applications of Data-Driven Robust Design
Organizer: Man-Chung Yue
Chair: Man-Chung Yue

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STO
Tue.2 H 2033
Large-Scale Stochastic First-Order Optimization (Part I)
Organizers: Filip Hanzely, Samuel Horvath
Chair: Samuel HorvathZheng Qu, The University of Hong Kong (joint work with Fei
Li)
Adaptive primal-dual coordinate descent methods for nonsmooth composite minimization with linear operator

Tue.3 14:45–16:00

We propose and analyse an inexact augmented Lagrangian (IAL) algorithm for solving large-scale composite, nonsmooth andconstrained convex optimization problems. Each subproblem
is solved inexactly with self-adaptive stopping criteria, without
requiring the target accuracy a priori as in many existing variants of I-AL methods. In addition, each inner problem is solved
by applying accelerated coordinate descent method, making
the algorithm more scalable when the problem dimension ishigh.
Pavel Dvurechensky, Weierstrass Institute for Applied Analysis
and Stochastics (WIAS) (joint work with Alexander Gasnikov,
Alexander Tiurin)
A Unifying Framework for Accelerated Randomized Optimization Methods
We consider smooth convex optimization problems withsimple constraints and inexactness in the oracle informationsuch as value, partial or directional derivatives of the objectivefunction. We introduce a unifying framework, which allows toconstruct different types of accelerated randomized methods
for such problems and to prove convergence rate theorems
for them. We focus on accelerated random block-coordinate
descent, accelerated random directional search, accelerated
random derivative-free method.
Xun Qian, KAUST (joint work with Robert Gower, Nicolas
Loizou, Peter Richtárik, Alibek Sailanbayev, Egor Shulgin)
SGD: General Analysis and Improved Rates
We propose a general yet simple theorem describing theconvergence of SGD under the arbitrary sampling paradigm.
Our analysis relies on the recently introduced notion of expected smoothness and does not rely on a uniform bound on
the variance of the stochastic gradients. By specializing ourtheorem to different mini-batching strategies, such as samplingwith replacement and independent sampling, we derive exact
expressions for the stepsize as a function of the mini-batch size.

Parallel Sessions Abstracts Tue.3 14:45–16:00

85

Tue.3 14:45–16:00

Güray Kara, Norwegian University of Science and Technology(joint work with Hossein Farahmand, Asgeir Tomasgard)
Optimal Scheduling and Bidding of Flexibility for a Portfolio of Power System Assets in a Multi-Market Setting
Flexibility is an important concept to cope with uncertainty
in energy systems. In this paper, we consider the problem of anactor that manages a portfolio of flexible assets. We present
a Stochastic Programming model for the dynamic schedulingof a portfolio for which energy and flexibility products canbe delivered into multiple markets like day-ahead, intraday,
and flexibility markets. Some of these products are locations
specific, while others are system-wide or even cross-border. The
purpose in the short-term is to remove bottlenecks and ensure
security of supply at the minimum cost.
Clovis Gonzaga, LACTEC (joint work with Elizabeth Karas,
Gislaine Periçaro)
Optimal non-anticipative scenarios for non-linear hydrothermal power systems
The long-term operation of hydro-thermal power systems
is modeled by a large-scale stochastic optimization problem
with non-linear constraints due to the head computation in hydroelectric plants. We state the problem as a non-anticipative
scenario analysis, leading to a large-scale non-linear programming problem. This is solved by a Filter-SQP algorithm whose
iterations minimize quadratic Lagrangian approximations using
exact Hessians in L∞ trust regions. The method is applied to
the long-term planning of the Brazilian system, with over 100
hydroelectric and 50 thermoelectric plants.
Alberto J. Lamadrid L., Lehigh University (joint work with Luis
Zuluaga)
Equilibrium prices for competitive electricity markets
We consider an electricity market in which uncertainty arises
from the presence of renewable energy sources. We consider arobust model for the market using chance constraints together
with a proportional control law for power generation. We provethat in this framework, market clearing prices yielding a robust
competitive market equilibrium, as well as revenue adequate
prices, can be computed and used for uncertain market settlements by risk-aware system operators. We contrast our results
to methods using a sample average stochastic program fromdesired prices can be obtained in each scenario.

BIG
Tue.3 H 0105
Recent Advancements in Optimization Methods for Machine Learning (Part I)
Organizers: Albert Berahas, Paul Grigas, Martin Takáč
Chair: Albert Berahas
Alexandre d’Aspremont, CNRS / École Normale Supérieure(joint work with Thomas Kerdreux, Sebastian Pokutta)
Restarting Frank-Wolfe
The vanilla Frank-Wolfe algorithm only ensures a worst-casecomplexity of O(1/ epsilon). Various recent results have shownthat for strongly convex functions, the method can be slightly
modified to achieve linear convergence. However, this still leaves
a huge gap between sublinear O(1/ epsilon) convergence andlinear O(log 1/ epsilon) convergence. Here, we present a newvariant of Conditional Gradients, that can dynamically adapt to
the function’s geometric properties using restarts and smoothlyinterpolates between the sublinear and linear regimes.
Heyuan Liu, University of California, Berkeley (joint work withPaul Grigas)[moved] New Methods for Regularization Path Optimization via Differential Equations
We develop and analyze several different second orderalgorithms for computing an approximately optimal solution/regularization path of a parameterized convex optimization
problem with smooth Hessian. Our algorithms are inspired bya differential equations perspective on the parametric solution
path and do not rely on the specific structure of the regularizer.
We present computational guarantees that bound the oraclecomplexity to achieve an approximately optimal solution pathunder different sets of smoothness assumptions and that also
hold in the presence of approximate subproblem solutions.
Lin Xiao, Microsoft Research (joint work with Junyu Zhang)
A Composite Randomized Incremental Gradient Method
We consider the problem of minimizing the composition of asmooth function (which can be nonconvex) and a smooth vector
mapping, where both of them can be expressed as the averageof a large number of components. We propose a composite randomized incremental gradient method based on a SAGA-typeof gradient estimator. The gradient sample complexity of our
method matches that of several recently developed methods
based on SVRG in the general case. However, for structured
problems where linear convergence rates can be obtained, ourmethod has much better complexity for ill-conditioned problems.

Tue.3 14:45–16:00

APP
Tue.3 H 1012
Applications in Energy (Part III)
Chair: Alberto J. Lamadrid L.

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Tue.3 14:45–16:00

VIS
Tue.3 H 0110
Quasi-Variational Inequalities and Generalized Nash Equilibrium Problems (Part I)
Organizers: Amal Alphonse, Carlos Rautenberg
Chair: Amal AlphonseCON
Tue.3 H 2032
Semidefinite Programming: Solution Methods and Applications
Organizer: Angelika Wiegele
Chair: Angelika Wiegele

Christian Kanzow, University of Würzburg (joint work with
Daniel Steck)
Quasi-Variational Inequalities in Banach Spaces: Theory
and Augmented Lagrangian Methods

Marianna De Santis, Sapienza University of Rome (joint workwith Martina Cerulli, Elisabeth Gaar, Angelika Wiegele)
Augmented Lagrangian Approaches for Solving Doubly
Nonnegative Programming Problems

This talk deals with quasi-variational inequality problems(QVIs) in a generic Banach space setting. We provide a theoretical framework for the analysis of such problems based on
the pseudomonotonicity (in the sense of Brezis) of the variational operator and a Mosco-type continuity of the feasible
set mapping. These assumptions can be used to establish theexistence of solutions and their computability via suitable approximation techniques. An augmented Lagrangian techniqueis then derived and shown to be convergent both theoreticallyand numerically.

Augmented Lagrangian methods are among the most popular
first-order approaches to handle large scale semidefinite programming problems. We focus on doubly nonnegative problems,
namely semidefinite programming problems where elements ofthe matrix variable are constrained to be nonnegative. Startingfrom two methods already proposed in the literature on conic
programming, we introduce Augmented Lagrangian methods
with the possibility of employing a factorization of the dual
variable. We present numerical results for instances of the DNN
relaxation of the stable set problem.

Steven-Marian Stengl, WIAS/HU Berlin
On the Convexity of Optimal Control Problems involving
Nonlinear PDEs or VIs

Christoph Helmberg, TU Chemnitz
T.he B.undle A.pproach – Scaling in ConicBundle 1.0 for
Convex and Conic Optimization

In recent years, generalized Nash equilibrium problems infunction spaces involving control of PDEs have gained increasing interest. One of the central issues arising is the questionof existence, which requires the topological characterization of
the set of minimizers for each player. In this talk, we propose
conditions on the operator and the functional, that guarantee
the reduced formulation to be a convex minimization problem.
Subsequently, we generalize the results of convex analysis toderive optimality systems also for nonsmooth operators. Ourfindings are illustrated with examples.

Bundle methods form a cutting model from collected subgradient information and determine the next candidate as theminimizer of the model augmented with a proximal term. The
choice of cutting model and proximal term offers a lot of flexibility and may even allow to move towards second order behavior.
After a short introduction to bundle methods in general, we
discuss the current approach for integrating such choices in theforthcoming ConicBundle 1.0 for the general and the semidefinite case and report on experience gathered on several test
instances.

Rafael Arndt, Humboldt-Universität zu Berlin (joint work withMichael Hintermüller, Carlos Rautenberg)
On quasi-variational inequality models arising in the description of sandpiles and river networks

Georgina Hall, INSEAD
Semidefinite Programming-Based Consistent Estimators
for Shape-Constrained Regression

We consider quasi-variational inequalities with pointwise
constraints on the gradient of the state, that arise in themodeling of sandpile growth, where the constraint depends
on local properties of the supporting surface and the solution.
We will demonstrate an example where multiple solutions
exist in the unregularized problem but not after regularization.
Solution algorithms in function space are derived, considering
two approaches: a variable splitting method and a semismooth
Newton method. The application can further be extended toregarding water-drainage as the limiting case.

In this talk, I consider the problem of shape-constrained regression, where response variables are assumed to be a function
f of some features (corrupted by noise) and f is constrained
to be monotonous or convex with respect to these features.
Problems of these type occur in many areas, such as, e.g., pricing. Using semidefinite programming, I construct a regressor
for the data which has the same monotonicity and convexity
properties as f . I further show that this regressor is a consistentestimator of f .

Parallel Sessions Abstracts Tue.3 14:45–16:00

Mehdi Karimi, University of Waterloo (joint work with Levent
Tuncel)Convex Optimization Problems in Domain-Driven Form:
Theory, Algorithms, and Software
In this talk, we introduce the Domain-Driven form for convex optimization problems and show how general it is by several examples. We have designed infeasible-start primal-dual
interior-point algorithms for the Domain-Driven form, which
accepts both conic and non-conic constraints. Our algorithms
enjoy many advantages of primal-dual interior-point techniques
available for conic formulations, such as the current best complexity bounds. At the end, we introduce our software package
DDS created based on our results for many classes of convex
optimization problems.
Riley Badenbroek, Tilburg University (joint work with Etiennede Klerk)
Simulated Annealing with Hit-and-Run for Convex Optimization
We analyze the simulated annealing algorithm by Kalai and
Vempala [Math of OR 31.2 (2006): 253-266] using the temperature schedule put forward by Abernethy and Hazan [arXiv
1507.02528v2, 2015]. This algorithm only assumes a membership oracle of the feasible set, and returns a solution inpolynomial time which is near-optimal with high probability.
Moreover, we propose a number of modifications to improve
the practical performance of this method. Numerical examples show the method has an application to convex problems
where no other method is readily available, such as copositive
programming.
David Papp, North Carolina State University (joint work with
Sercan Yildiz)
alfonso: A new conic solver for convex optimization overnon-symmetric cones
Many optimization problems can be conveniently written
as conic programs over non-symmetric cones; however, these
problems are lacking the solver support that symmetric conic
optimization problems have. We propose alfonso, a highly general Matlab-based interior point solver for non-symmetric conic
optimization. Alfonso can solve optimization problems over acone as long as the user can supply the gradient and Hessianof a logarithmically homogeneous self-concordant barrier for
either the cone or its dual. We demonstrate its efficiency using
non-symmetric cones arising in polynomial programming

CNV
Tue.3 H 1028
Operator Splitting and Proximal Decomposition Methods
Organizers: Patrick L. Combettes, Jonathan Eckstein
Chair: Patrick L. Combettes
Yu Du, University of Colorado DenverAn outer-inner linearization method for non-convex andnon-differentiable composite regularization problems
We propose a new outer-inner linearization method for nonconvex statistical learning problems involving nonconvex structural penalties and nonconvex loss. It linearizes the outerconcave functions and solves the resulting convex, but still
nonsmooth, subproblems by a special alternating linearization method. We provide proof of convergence of the method
under mild conditions. Finally, numerical examples involving
nonconvex structural penalties and nonconvex loss functions
demonstrate the efficacy and accuracy of the method.
Maicon Marques Alves, Federal University of Santa Catarina UFSC (joint work with Jonathan Eckstein, Marina Geremia)
Relative-Error Inertial-Relaxed Inexact Versions of
Douglas-Rachford and ADMM Splitting Algorithms
This paper derives new inexact variants of the DouglasRachford splitting method for maximal monotone operatorsand the alternating direction method of multipliers (ADMM)
for convex optimization. The analysis is based on a new inexact
version of the proximal point algorithm that includes both aninertial step and and overrelaxation. We apply our new inexact
ADMM method to LASSO and logistic regression problems andobtain better computational performance than earlier inexact
ADMM methods.
Jonathan Eckstein, Rutgers University (joint work with Patrick
Johnstone)
Projective Splitting with Co-coercive Operators
This talk describes a new variant of monotone operator
projective splitting in which co-coercive operators can be processed with a single forward step per iteration. This result
establishes a symmetry between projective splitting algorithms,
the classical forward-backward method, and Tseng’s forwardbackward-forward method: In a situation in which Lipschitz
monotone operators require two forward steps, co-coercive
operators may be processed with a single forward step. The
single forward step may be interpreted as a single step of
the classical forward-backward method for the standard “prox”
problem associated with the cocoercive operator, starting at
the previous known point in the operator graph. Proving convergence of the algorithm requires some departures from theusual proof framework for projective splitting. We have also
developed a backtracking variant of the method for cases inwhich the co-coercivity constant is unknown, and present somecomputational tests of this method on large-scale optimization
problems from data fitting and portfolio applications.

Tue.3 14:45–16:00

CON
Tue.3 H 3006
Advances in Interior Point Methods for Convex Optimization
Organizer: Etienne de Klerk
Chair: Etienne de Klerk

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CNV
Tue.3 H 1029
Matrix Optimization Problems: Theory, Algorithms andApplications
Organizer: Chao Ding
Chair: Chao Ding

DER
Tue.3 H 2038
Emerging Trends in Derivative-Free Optimization (Part I)
Organizers: Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Chair: Anne Auger

Xinyuan Zhao, Beijing University of Technology (joint workwith Chao Ding, Zhuoxuan Jiang)
A DC algorithm for quadratic assignment problem

Christine Shoemaker, National University of Singapore (joint
work with Yichi Shen)
Global optimization of noisy multimodal functions with
RBF surrogates

Tue.3 14:45–16:00

We show that the quadratic assignment problem (QAP) isequivalent to the doubly nonnegative (DNN) problem with arank constraint. A DC approach is developed to solve that DNN
problem. And its subproblems are solved by the semi-proximal
augmented Lagrangian method. The sequence generated by
the proposed algorithm converges to a stationary point of
the DC problem with a suitable parameter. Numerical results
demonstrate: for some examples of QAPs, the optimal solutions
can be found directly; for the others, good upper bounds withthe feasible solutions are provided.
Xudong Li, Fudan University (joint work with Liang Chen, Defeng Sun, Kim-Chuan Toh)
On the Equivalence of Inexact Proximal ALM and ADMM
for a Class of Convex Composite Programming
In this talk, we show that for a class of linearly constrained
convex composite optimization problems, an (inexact) symmetric Gauss-Seidel based majorized multi-block proximal alternating direction method of multipliers (ADMM) is equivalent
to an inexact proximal augmented Lagrangian method (ALM).
This equivalence not only provides new perspectives for understanding some ADMM-type algorithms but also supplies
meaningful guidelines on implementing them to achieve better
computational efficiency.
Chao Ding, AMSS, Chinese Academy of Sciences
Perturbation analysis of matrix optimization
The nonsmooth composite matrix optimization problem, inparticular, the matrix norm optimization problem, is a generalization of the matrix conic program with wide applications innumerical linear algebra, computational statistics and engineering. This talk is devoted to study some perturbation propertiesof matrix optimization problems

We consider the optimization of multimodal, computationallyexpensive noisy functions f (x) with the assistance of a Radial
Basis Function (RBF) surrogate approximation of f (x). ThisRBF surrogate is then used to help guide the optimization
search to reduce the number of evaluations required. Because
the function is noisy, it is necessary to resample some points
to estimate the mean variance of f (x) at each location. The
results compare favorably to Bayesian Optimization methods
for noisy functions and can be more flexible in the choice ofobjective function.
Nikolaus Hansen, Inria / Ecole Polytechnique (joint work with
Youhei Akimoto)Diagonal acceleration for covariance matrix adaptation
evolution strategies
We introduce an acceleration for covariance matrix adaptation evolution strategies (CMA-ES) by means of adaptive diagonal decoding. This diagonal acceleration endows the default
CMA-ES with the advantages of separable CMA-ES without
inheriting its drawbacks. Diagonal decoding can learn a rescaling of the problem in the coordinates within linear number offunction evaluations. It improves the performance, and even thescaling, of CMA-ES on classes of non-separable test functions
that reflect, arguably, a landscape feature commonly observedin practice.
Giacomo Nannicini, IBM T.J. Watson
The evolution of RBFOpt: improving the RBF method,with one eye on performanceThis talk will discuss several improvements to the opensource library RBFOpt for surrogate model based optimization.
RBFOpt is used in commercial applications and practical needs
drove its development. We will discuss efficient model selection,
local search, parallel optimization and – time permitting –multi objective optimization. Part of this talk is devoted toengineering issues, but we will try to be precise regarding themathematics for efficient model selection and local search. Thisis based on results scattered in various recent papers; somedetails are currently unpublished.

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GLO
Tue.3 H 3002
Recent Developments in Structured Difference-Of-Convex
Optimization
Organizer: Zirui Zhou
Chair: Zirui Zhou

MUL
Tue.3 H 3013
Recent Developments in Set Optimization (Part III)
Organizers: Akhtar A. Khan, Elisabeth Köbis, Christiane Tammer
Chair: Christiane Tammer

Akiko Takeda, The University of Tokyo/RIKEN (joint workwith Jun-ya Gotoh, Katsuya Tono)
DC Formulations and Algorithms for Sparse Optimization
Problems

Markus Arthur Köbis, Free University Berlin
Set optimization in systems biology: application to dynamic resource allocation problems

We address the minimization of a smooth objective function
under an L0 -norm constraint and simple constraints. Some
efficient algorithms are available when the problem has noconstraints except the L0 -norm constraint, but they often become inefficient when the problem has additional constraints.
We reformulate the problem by employing a new DC (difference of two convex functions) representation of the L0 -norm
constraint, so that DC algorithm can retain the efficiency byboiling down its subproblems to the projection operation onto
a simple set.
Hongbo Dong, Washington State University (joint work withMin Tao)
On the Linear Convergence of DC Algorithms to Strong
Stationary Points [canceled]
We consider the linear convergence of algorithms for structured DC optimization. We allow nonsmoothness in both of theconvex and concave components, with a finite max structurein the concave component, and focus on algorithms computing d(irectional)-stationary points. Our results are based onstandard error bounds assumptions and a new locally linear
regularity regarding the intersection of certain stationary sets
and dominance regions. A by-product of our work is a unified
description of strong stationary points and global optimum byusing the notion of approximate subdifferential.

We will address how set-valued optimization can be used toapproach problems from systems biology and that way opens
doorways to simplifying and systematizing many, often pragmatic frameworks in this field. We will concentrate on problems
concerning the dynamic simulation of metabolic networks withpotential applications ranging from personalized medicine overbio-fuel production to ecosystem biology. From the mathematical modeling viewpoint, we will underline how evolutionary
principles may in this regard be interpreted as a regularizationand illustrate this by some small-scale examples.
Elisabeth Köbis, Martin Luther University Halle-Wittenberg(joint work with César Gutiérrez, Lidia Huerga, Christiane Tammer)
A scalarization scheme in ordered sets with applications
to set-valued and robust optimization
In this talk, a method for scalarizing optimization problems
whose final space is an ordered set is stated without assuming
any additional assumption on the data of the problem. By thisapproach, nondominated and minimal solutions are characterized in terms of solutions of scalar optimization problems whose
objective functions are the post-composition of the original
objective with scalar functions satisfying suitable properties.
The obtained results generalize some recent ones stated inquasi ordered sets and real topological linear spaces.
Akhtar A. Khan, Rochester Institute of Technology
Analysing the Role of the Inf-Sup Condition in Inverse
Problems for Saddle Point Problems
We will discuss the inverse problem of parameter identification in general saddle point problems that frequently appear
in applied models. We focus on examining the role of the infsup condition. Assuming the saddle point problem is solvable,
we study the differentiability of the set-valued parameter-tosolution map by using the first-order and the second-ordercontingent derivatives. We investigate the inverse problem
by using the nonconvex output least-squares and the convex
modified output least-squares objectives. Numerical results andapplications will be presented.

Tue.3 14:45–16:00

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NON

NON

Tue.3 14:45–16:00

Tue.3 H 0104
Recent Advances in First-Order Methods (Part II)
Organizers: Guanghui Lan, Yuyuan Ouyang, Yi Zhou
Chair: Yuyuan Ouyang

Tue.3 H 2013
Stochastic Methods for Nonsmooth Optimization
Organizer: Konstantin Mishchenko
Chair: Konstantin Mishchenko

Mert Gurbuzbalaban, Rutgers University
A Universally Optimal Multistage Accelerated Stochastic
Gradient Method

Tianyi Lin, University of California, Berkeley
Improved oracle complexity for stochastic compositionalvariance reduced gradient

We study the problem of minimizing a strongly convex andsmooth function when we have noisy estimates of its gradient.
We first provide a framework that can systematically trade-off
acceleration and robustness to gradient errors. Building on ourframework, we propose a novel multistage accelerated algorithm
that is universally optimal in the sense that it achieves the
optimal rate both in the deterministic and stochastic case andoperates without knowledge of noise characteristics.

We propose an accelerated stochastic compositional variance reduced gradient method for optimizing the sum of acomposition function and a convex nonsmooth function. We
provide an incremental first-order oracle (IFO) complexity analysis for the proposed algorithm and show that it is provably
faster than all the existing methods. In particular, we showthat our method achieves an asymptotic IFO complexity ofO((m + n) log(1/ eps) + 1/ eps3 ) where m and n are thenumber of inner/outer component functions, improving thebest-known results of O(m + n + (m + n)2/3 / eps2 ) for convex
composition problem.

Hilal Asi, Stanford University (joint work with John Duchi)
The importance of better models in stochastic optimization
Standard stochastic optimization methods are brittle, sensitive to algorithmic parameters, and exhibit instability outside ofwell-behaved families of objectives. To address these challenges,
we investigate models that exhibit better robustness to problem families and algorithmic parameters. With appropriately
accurate models, which we call the aProx family, stochastic
methods can be made stable, provably convergent and asymptotically optimal. We highlight the importance of robustness
and accurate modeling with a careful experimental evaluationof convergence time and algorithm sensitivity.
Damien Scieur, SAIT Montreal (joint work with Raghu Bollapragada, Alexandre d’Aspremont)
Nonlinear Acceleration of Momentum and Primal-Dual Algorithms
We describe a convergence acceleration scheme for multistep
optimization algorithms. The extrapolated solution is written
as a nonlinear average of the iterates produced by the original
optimization algorithm. Our scheme handles algorithms with
momentum terms such as Nesterov’s accelerated method, or
primal-dual methods. The weights are computed via a simple
linear system and we analyze performance in both online andoffline modes. We use Crouzeix’s conjecture to show that
acceleration performance is controlled by the solution of a
Chebyshev problem.

Adil Salim, King Abdullah University of Science and Technology(KAUST)
On the Convergence of some Stochastic Primal-Dual Algorithms
Primal-dual algorithms, like Chambolle-Pock or Vu-Condat
algorithms, are suitable methods to solve nonsmooth optimization problems involving linear constraints. In this talk, we willconsider stochastic versions of these algorithms where every
function used to define the problem is written as an expectation, including the constraints. Asymptotic and non-asymptotic
convergence results involving the expected primal-dual gap will
be provided. These results cover some recent findings regarding
stochastic proximal (gradient) algorithms.
Konstantin Mishchenko, KAUST (joint work with Peter
Richtárik)
Variance Reduction for Sums with Smooth and Nonsmooth Components with Linear Convergence
We present a stochastic variance reduction method for convex sums with smooth and nonsmooth entries. In the casewhere the smooth part is strongly convex and the nonsmooth
entries are of the form gj (Aj x) the method achieves linear convergence with rate O(max(n, κ, λmin (A)) ∗ log(1/)), where
n is the number of smooth terms, κ is the conditioning andλmin (A) is the smallest eigenvalue of the matrix constructedfrom the rows of A1 , A2 , etc. Without these assumption, themethod has rate O(1/t) which improves to O(1/t2 ) understrong convexity.

Parallel Sessions Abstracts Tue.3 14:45–16:00

Yuji Nakatsukasa, University of Oxford (joint work with Paul
Goulart, Nikitas Rontsis)
Eigenvalue perturbation theory in optimization: accuracyof computed eigenvectors and approximate semidefinite
projection
Eigenvalue perturbation theory is a classical subject in matrix
analysis and numerical linear algebra, but seemingly has had
little impact in optimization. This talk presents recent progressin eigenvalue perturbation theory, highlighting its roles in optimization. We first derive sharp perturbation and error bounds
for Ritz vectors (approximate eigenvectors) and approximate
singular vectors, which are used e.g. in SDP, PCA and trustregion subproblems. We also present bounds on the accuracyof approximate semidefinite projection, motivated by operator
splitting methods in optimization.
Zsolt Csizmadia, FICO
Eliminations and cascading in nonlinear optimization
The talk explores the numerical implications of using variable
eliminations in nonlinear problems versus applying intermediatevariables; looking at implications on feasibility, formula sizes
or the size of the auto differentiation stacks. For boundedcases when eliminations are not applicable, the talk will present
cascading, a technology originating from classical refinery optimization models but recently successfully applied to financial
problems. Detection methods of elimination candidates andcascading structures will be presented.
John Pearson, University of Edinburgh
Interior point methods and preconditioners for PDEconstrained optimization
In this talk we consider the effective numerical solution of
PDE-constrained optimization problems with additional box
constraints on the state and control variables. Upon discretization, a sensible solution strategy is to apply an interior point
method, provided one can solve the large and structured matrix systems that arise at each Newton step. We therefore
consider fast and robust preconditioned iterative methods forthese systems, examining two cases: (i) with L2 norms arising
within the cost functional; (ii) with an additional L1 norm term
promoting sparsity in the control variable.

IMP
Tue.3 H 0111
Advances in Linear Programming
Chair: Daniel Rehfeldt
Julian Hall, University of Edinburgh
HiGHS: a high-performance linear optimizer
This talk will present HiGHS, a growing open-source repository of high-performance software for linear optimization basedon award-winning computational techniques for the dual simplex method. The talk will give an insight into the work which
has led to the creation of HiGHS and then set out the features
which allows it to be used in a wide range of applications.
Plans to extend the class of problems which can be solved
using HiGHS will be set out.
Ivet Galabova, University of Edinburgh
Solution of quadratic programming problems for fast approximate solution of linear programming problems
Linear programming problems (LP) are widely solved inpractice and reducing the solution time is essential for many
applications. This talk will explore solving a sequence of unconstrained quadratic programming (QP) problems to derive
an approximate solution and bound on the optimal objective
value of an LP. Techniques for solving these QP problems fast
will be discussed.
Daniel Rehfeldt, Technische Universität Berlin (joint work with
Ambros Gleixner, Thorsten Koch)
Solving large-scale doubly bordered block diagonal LPs inparallel
We consider LPs of block diagonal structure with linking variables and constraints, motivated by energy system applications.
To solve these problems, we have extended the interior-point
solver PIPS-IPM (for stochastic QPs). This talk focuses on
methods to handle large numbers of linking variables and constraints. A preconditioned iterative approach and a hierarchicalalgorithm will be presented, both of which exploit structure
within the linking part. The extensions allow us to solve realworld problems with over 500 million variables and constraints
in a few minutes on a supercomputer.

Tue.3 14:45–16:00

NON
Tue.3 H 2053
Interplay of Linear Algebra and Optimization (Part II)
Organizers: Robert Luce, Yuji Nakatsukasa
Chair: Robert Luce

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PDE

PDE

Tue.3 14:45–16:00

Tue.3 H 0106
Computational Design Optimization (Part III)
Organizers: Martin Siebenborn, Kathrin Welker
Chair: Martin Siebenborn

Tue.3 H 0107
Optimal Control and Dynamical Systems (Part III)
Organizers: Cristopher Hermosilla, Michele Palladino
Chair: Cristopher Hermosilla

Estefania Loayza-Romero, Chemnitz University of Technology(joint work with Ronny Bergmann, Roland Herzog)
A discrete shape manifold and its use in PDE-constrained
shape optimization

Matthias Knauer, Universität Bremen (joint work with Christof
Büskens)
Real-Time Approximations for Discretized Optimal Control Problems using Parametric Sensitivity Analysis

This work aims to present the novel notion of discrete shape
manifold, which will allow solving PDE-constrained shape optimization problems using a discretize-then-optimize approach.
This manifold will be endowed with a complete Riemannian
metric that prevents mesh destruction. The computation of
discrete shape derivatives and its relation with the optimizethen-discretize approach will be discussed. Finally, we will
present the application of this approach to the solution of
simple 2D optimization problems.

In many industrial applications solutions of optimal control
problems are used, where the need for low computational times
outweighs the need for absolute optimality. For solutions of fully
discretized optimal control problems we propose two methods toapproximate the solutions of problems with modified parametervalues in real-time by using sensitivity derivatives. Using the
NLP solver WORHP, the quality and applicability of bothmethods are illustrated by the application of an oscillation freemovement of a container crane in a high rack warehouse.

Veronika Schulze, Paderborn University (joint work with Benjamin Jurgelucks, Andrea Walther)
Increasing Sensitivity of Piezoelectric Ceramics by Electrode Shape Optimization

Tan Cao, State University of New York–Korea (joint work with
Boris Mordukhovich)
Optimal Control for a Nonconvex Sweeping Process with
Applications to the Crowd Motion Model [canceled]

Piezoelectricity defines the relation between electrical andmechanical changes in a specimen and can be described bya damped PDE system. Often material parameters of given
piezoelectrics are not known precisely, but are required to beaccurate. The solution of an inverse problem provides thesevalues. For this purpose, sensitivities with respect to the material parameters can be calculated. In order to compute allparameters the sensitivities of some specific parameters must
be increased. This is achieved by adjusting the electrode shapeswith shape and topology optimization techniques.

The talk concerns the study a new class of optimal control
problems of (Moreau) sweeping process. A major motivation forour study of such challenging optimal control problems comesfrom the application to the crowd motion model in a practically adequate planar setting with nonconvex but prox-regular
sweeping sets. Based on a constructive discrete approximation
approach and advanced tools of variational analysis and generalized differentiation, we derive necessary optimality conditions
for discrete-time and continuous-time sweeping control systems
expressed entirely via the problem data.

Christian Kahle, Technische Universität München (joint workwith Harald Garcke, Michael Hinze, Kei-Fong Lam)
The phase field approach for topology optimization

Gerardo Sanchez Licea, Facultad de Ciencias UNAM
Sufficiency for purely essentially bounded singular optimal
controls

In a given domain Ω we search for a fluid domain E, such thatan objective depending on E and the velocity and the pressure
field in E is minimized. We describe the distribution of the fluidand void domain by a phase field variable ϕ ∈ H 1 (Ω) ∩ L∞ (Ω)
that encodes the subdomains by ϕ(x) = ±1. Additionaly we use
a porosity approach to extend the Navier–Stokes equation fromE to Ω. As solution algorithm we apply the variable metricprojection type method from [L. Blank and C. Rupprecht,
SICON 2017, 55(3)].

In this talk we study certain classes of fixed end-point optimal control problems of Lagrange with nonlinear dynamics,
inequality and equality isoperimetric restraints and pointwise
mixed time-state-control inequality and equality constraints.
The diminishing of the gap between the classical necessary andsufficient conditions for optimality becomes the main novelty ofthe talk. In fact, we illustrate the properties of the new theory
by exhibiting some singular and purely essentially bounded
optimal controls satisfying all conditions of the corresponding
sufficiency theorems.

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PDE
Tue.3 H 0112
Recent Advances in PDE Constrained Optimization(Part II)
Organizer: Axel Kröner
Chair: Axel Kröner

ROB
Tue.3 H 1058
Distributionally Robust Optimization: Computation and Insights
Organizer: Karthik Natarajan
Chair: Karthik Natarajan

Meenarli Sharma, Indian Institute of Technology Bombay (joint
work with Sven Leyffer, Lars Ruthotto, Bart van Bloemen
Waanders)
Inversion of Convection-Diffusion PDE with Discrete
Sources

Jianqiang Cheng, University of Arizona
Computationally Efficient Approximations for Distributionally Robust Optimization [canceled]

We aim to find the number and location of sources fromdiscrete measurements of concentration on computational domain. We formulate a forward PDE model and then invert
it for the unknown sources by reformulating the latter as a
MINLP, resulting in mixed-integer PDE constrained optimization (MIPDECO). For MINLP solvers, solving such MINLPs
is computationally prohibitive for fine meshes and 3D cases.
We propose a trust-region improvement heuristic that uses
continuous relaxation of MIPDECO problem with different
rounding schemes.
Johanna Katharina Biehl, TU Darmstadt (joint work with Stefan Ulbrich)
Adaptive Multilevel Optimization with Application to
Fluid-Structure Interaction
We present a multilevel optimization algorithm for PDEconstrained problems. The basis of the algorithm is an adaptive
SQP method, using adaptive grid refinement to reduce thecomputational cost. This method is extended by introducing
a level build with reduced order models (ROM) for state andadjoint equation. For this algorithm we can show convergence,
when suitable a posteriori error estimators on the discretizationand the ROM are available. The algorithm is applied to abenchmark problem of fluid-structure interaction, which is aproblem of interest in many engineering applications.
Johann Michael Schmitt, TU Darmstadt (joint work with Stefan Ulbrich)
Optimal boundary control of hyperbolic balance laws withstate constraints
We study optimal control problems governed by balance lawswith initial and boundary conditions and pointwise state constraints. The boundary data switch between smooth functions
at certain points, where we consider the smooth parts and theswitching points as control. State constraints are challenging
in this context since solutions of nonlinear hyperbolic balancelaws may develop shocks after finite time which prohibits theuse of standard methods. We derive optimality conditions andprove convergence of the Moreau-Yosida regularization that isused to treat the state constraints.

Many distributionally robust optimization (DRO) instances
can be formulated as semidefinite programming (SDP) problems. However, SDP problems in practice are computationallychallenging. In this talk, we present computationally efficient(inner and outer) approximations for DRO problems basedon the idea of the principal component analysis (PCA). We
also derive theoretical bounds on the gaps between the original problem and its PCA approximations. Furthermore, an
extensive numerical study shows the strength of the proposed
approximations in terms of solution quality and runtime.
Divya Padmanabhan, Singapore University of Technology and
Design (joint work with Karthik Natarajan, Arjun Ramachandra)
Bounds on Sums of Dependent Random Variables: An Optimization Approach
Computing tight bounds for the probability of sums of dependent variables is a fundamental problem with several applications. In this work we study the problem of computing theprobability of a sum of dependent Bernoulli random variables
exceeding k, from a DRO perspective. We study the problem under known univariate and bivariate marginals with tree
structure, where a consistent joint distribution is guaranteed
to exist. We propose exact and compact linear programming
formulations for these sparse forms. This is based on joint workwith Arjun Ramachandra and Karthik Natarajan at SUTD.
Karthik Natarajan, Singapore University of Technology and
Design (joint work with Bikramjit Das, Anulekha Dhara)On the Heavy-Tail Behavior of the Distributionally Robust
Newsvendor
Since the seminal work of Scarf (1958) on the newsvendor
problem with ambiguity in the demand distribution, there has
been a growing interest in the study of the distributionallyrobust newsvendor problem. A simple observation shows thatthe optimal order quantity in Scarf’s model with known firstand second moment is also optimal for a heavy-tailed censored
student-t distribution with degrees of freedom 2. In this paper,
we generalize this “heavy-tail optimality” property of the distributionally robust newsvendor to a more general ambiguity
set.

Tue.3 14:45–16:00

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Tue.3 14:45–16:00

ROB
Tue.3 H 3012
Distributionally Robust Optimization
Organizer: Soroosh Shafieezadeh Abadeh
Chair: Soroosh Shafieezadeh Abadeh

SPA
Tue.3 H 3007
Continuous Systems in Information Processing (Part I)
Organizer: Martin Benning
Chair: Martin Benning

Tobias Sutter, École Polytechnique Fédérale de Lausanne(EPFL) (joint work with Daniel Kuhn, John Lygeros, Peyman
Mohajerin Esfahani)
Data-driven Approximate Dynamic Programming

Elena Celledoni, Norwegian University of Science and Technology
Deep learning as optimal control problems: models andnumerical methods

We consider linear programming (LP) problems in infinite
dimensional spaces and develop an approximation bridge fromthe infinite-dimensional LP to tractable finite convex programs
in which the performance of the approximation is quantified
explicitly. We illustrate our theoretical results in the optimal
control problem for Markov decision processes on Borel spaces
when the dynamics are unknown and learned from data andderive a probabilistic explicit error bound between the datadriven finite convex program and the original infinite linear
program.

We consider recent work by Haber and Ruthotto, where deeplearning neural networks are viewed as discretisations of an
optimal control problem. We review the first order conditions
for optimality, and the conditions ensuring optimality after
discretization. This leads to a class of algorithms for solving
the discrete optimal control problem which guarantee that thecorresponding discrete necessary conditions for optimality are
fulfilled. We discuss two different deep learning algorithms andmake a preliminary analysis of the ability of the algorithms togeneralize.

Nian Si, Stanford University (joint work with Jose Blanchet,
Karthyek Murthy)
Asymptotic Normality and Optimal Confidence Regions
in Wasserstein Distributionally Robust Optimization

Robert Tovey, University of Cambridge (joint work with Martin Benning, Carola-Bibiane Schönlieb, Evren Yarman, OzanÖktem)Continuous optimisation of Gaussian mixture models ininverse problems

We provide a theory of confidence regions for Wasserstein
distributionally robust optimization (DRO) formulations. In
addition to showing the asymptotic normality of these types of
estimators (under natural convex constraints), we also characterize optimal confidence regions (in a certain sense informedby the underlying DRO formulation and study the asymptotic
shape of these regions in a suitable topology defined for convergence of sets.
Soroosh Shafieezadeh Abadeh, EPFL (joint work with DanielKuhn, Peyman Mohajerin Esfahani, Viet Anh Nguyen)
Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization
We bridge the Bayesian and the minimax mean square error
estimators by leveraging tools from modern distributionallyrobust optimization. We show that the optimal estimator andthe least favorable distribution form a Nash equilibrium for the
Wasserstein ambiguity set and can be computed by solving asingle tractable convex program. We further devise a FrankWolfe algorithm with a linear convergence rate to solve theconvex program whose direction-searching subproblem canbe solved in a quasi-closed form. Using these ingredients, we
introduce an image denoiser and a robust Kalman filter.

In inverse problems it is common to include a convex optimisation problem which enforces some sparsity on the reconstruction, e.g. atomic scale Electron tomography. One draw-backto this, in the era of big data, is that we are still required tostore and compute using each of those coefficients we expect
to be zero. In this talk, we address this by parametrising thereconstruction as a sparse Gaussian Mixture Model and fitting, on a continuous grid, to the raw data. We will discuss
the pros (computational complexity) and overcoming the cons
(introduction of non-convexity) of such an approach.
Martin Benning, Queen Mary University of London (joint workwith Martin Burger, Elena Resmerita)
Iterative regularisation of continuous inverse problems via
an entropic projection method
In this talk, we discuss iterative regularisation of continuous,
linear ill-posed problems via an entropic projection method.
We present a detailed convergence analysis of the scheme that
respects the continuous nature of the underlying problem andwe present numerical results for continuous and ill-posed inverse
problems to support the theory.

Parallel Sessions Abstracts Tue.3 14:45–16:00

95

STO
Tue.3 H 2033
Large-Scale Stochastic First-Order Optimization (Part II)
Organizers: Filip Hanzely, Samuel Horvath
Chair: Samuel Horvath
Nidham Gazagnadou, Télécom ParisTech (joint work with
Robert Gower, Joseph Salmon)
Optimal mini-batch and step sizes for SAGA

Hamed Sadeghi, Lund University (joint work with Pontus Giselsson)
Acceleration of reduced variance stochastic gradientmethod [canceled]
We propose a convergence acceleration scheme for optimization problems in stochastic settings. We use Anderson
acceleration in combination with a reduced variance stochastic
gradient method and show that the iterates are stochasticallyquasi-Fejér monotone. This allows us to prove almost sure convergence of the method. We apply the acceleration techniqueto reduced variance stochastic gradient algorithms SVRG and
SAGA to show its performance gains.
Samuel Horvath, King Abdullah University of Science and Technology
Stochastic Distributed Learning with Gradient Quantization and Variance Reduction
We consider distributed optimization where the objectivefunction is spread among different devices, each sending incremental model updates to a central server. To alleviate the communication bottleneck, recent work proposed various schemes
to compress (e.g. quantize or sparsify) the gradients, thereby
introducing additional variance, that might slow down convergence. We provided a unified analysis of these approaches
in strongly/weakly convex and non-convex frameworks andprovide the first methods that achieve linear convergence forarbitrary quantized updates.

Tue.4 16:30–17:45

Recently, it has been shown that the step sizes of a family of
stochastic variance reduced gradient methods, called the JacSketch methods, depend on the expected smoothness constant.
We provide closed form expressions for this constant, leading tolarger step sizes and thus to faster convergence, and numericalexperiments verifying these bounds. We suggest a new practicefor setting the mini-batch and step sizes for SAGA, part of
the JacSketch family. Furthermore, we can now show that the
total complexity of the SAGA algorithm decreases linearly inthe mini-batch size up to an optimal value.

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Tue.4 16:30–17:45
Memorial Session
Tue.4 H 0105
Reflections on the Life and Work of Andrew R. Conn
Chair: Michael Overton
Andrew R. Conn passed away on March 14, 2019. He playeda pioneering role in nonlinear optimization and remained very
active in the field until shortly before his death. At this session,
five of his friends and colleagues will give 10 minutes tributes
to his work and his life. These will be followed by 30 minutes
where everyone will have the opportunity to contribute a few
words in memory of Andy, as he was always known. The five
speakers are:
Philippe L. Toint, University of Namur
Katya Scheinberg, Lehigh University
Annick Sartenaer, University of Namur
Henry Wolkowicz, University of Waterloo

APP
Tue.4 H 1028
Applications in Energy (Part IV)
Chair: Orcun Karaca
Julien Vaes, University of Oxford (joint work with Raphael
Hauser)Optimal Execution Strategy Under Price and Volume Uncertainty
In the seminal paper on optimal execution of portfolio transactions, Almgren and Chriss define how to liquidate a fixedvolume of a single security under price uncertainty. Yet sometimes, as in the power market, the volume to be traded can only
be estimated and becomes more accurate when approaching
the delivery time. We propose a model that accounts for volume
uncertainty and show that a risk-averse trader has benefit indelaying trades. With the incorporation of a risk, we avoid thehigh complexity associated to dynamic programming solutions
while yielding to competitive performance.

James Burke, University of Washington
More information about this memorial session on page 15.
Tue.4 16:30–17:45

Harald Held, Siemens, Corporate Technology (joint work with
Christoph Bergs)
MINLP to Support Plant Operators in Energy Flexibilization
Many production industries require energy, e.g. in form ofelectricity, heat, or compressed air. With the rise of renewableenergies and storage technologies, the question arises how suchindustries can take advantage of it, and to get an idea of energy
flexibilization potentials at all. In this talk, we will introduce asoftware tool that is currently being developed at Siemens thatallows industry users to assess their flexibilization potentials,
and also to ensure cost-optimal operation of production systems. The underlying optimization problem is a Mixed-Integer
NonLinear Program (MINLP).
Orcun Karaca, ETH Zürich (joint work with Maryam Kamgarpour)Core-Selecting Stochastic Auctions
Core-selecting auctions are well-studied in the deterministic
case for their coalition-proofness and competitiveness. Recently,
stochastic auctions have been proposed as a method to integrate intermittent participants in electricity markets. In thistalk, we define core-selecting stochastic auctions and show
their properties depending on the way uncertainty is accounted
for in the payment mechanism. We illustrate a trade-off between achieving desirable properties, such as cost-recovery andbudget-balance, in expectation or in every scenario. Our results
are verified with case studies.

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97

BIG
Tue.4 H 2013
Recent Advancements in Optimization Methods for Machine Learning (Part II)
Organizers: Albert Berahas, Martin Takáč
Chair: Albert BerahasBIG
Tue.4 H 3004
Recent Advances in Algorithms for Large-Scale Structured
Nonsmooth Optimization (Part II)
Organizer: Xudong Li
Chair: Xudong Li

Sebastian U. Stich, EPFL
Communication Efficient Variants of SGD for Distributed
Training

Ling Liang, National University of Singapore (joint work with
Kim-Chuan Toh, Quoc Tran-Dinh)
A New Homotopy Proximal Variable-Metric Framework for
Composite Convex Minimization

Martin Takáč, Lehigh University (joint work with Albert Berahas, Majid Jahani)
Quasi-Newton Methods for Deep Learning: Forget the
Past, Just Sample
We present two sampled quasi-Newton methods for deeplearning: sampled LBFGS (S-LBFGS) and sampled LSR1 (SLSR1). Contrary to the classical variants of these methods thatsequentially build Hessian or inverse Hessian approximations
as the optimization progresses, our proposed methods sample
points randomly around the current iterate at every iteration
to produce these approximations. As a result, the approximations constructed make use of more reliable (recent and local)
information, and do not depend on past iterate informationthat could be significantly stale.
Nicolas Loizou, The University of Edinburgh (joint work with
Mahmoud Assran, Nicolas Ballas, Mike Rabbat)
Stochastic Gradient Push for Distributed Deep Learning
Distributed data-parallel algorithms aim to accelerate thetraining of deep neural networks by parallelizing the computation of large mini-batch gradient updates across multiple nodes.
In this work, we study Stochastic Gradient Push (SGP) which
combines PushSum gossip protocol with stochastic gradient
updates. We prove that SGP converges to a stationary point ofsmooth, non- convex objectives at the same sub-linear rate as
SGD, that all nodes achieve consensus, and that SGP achieves
a linear speedup with respect to the number of nodes. Finally,
we empirically validate the performance of SGP.

This paper suggests a novel idea to develop new proximal
variable-metric methods for solving a class of composite convex problems. The idea is to utilise a new optimality condition parameterization to design a class of homotopy proximal
variable-metric methods that can achieve linear convergenceand finite global iteration-complexity bounds. Moreover, rigorous proofs of convergence results are also provided. Numericalexperiments on several applications are given to illustrate ourtheoretical and computational advancements when compare
to other state-of-the-art algorithms.
Meixia Lin, National University of Singapore (joint work withDefeng Sun, Kim-Chuan Toh, Yancheng Yuan)
On the Closed-form Proximal Mapping and Efficient Algorithms for Exclusive Lasso Models
The exclusive lasso regularization based on the `1,2 norm has
become popular due to its superior performance, which resultsin both inter-group and intra-group sparsity. In this paper,
we derive a closed-form solution for the proximal mapping ofthe `1,2 norm and its generalized Jacobian. Then we design
efficient first and second order algorithms for machine learningmodels involving the exclusive lasso regularization.
Krishna Pillutla, University of Washington (joint work with
Zaid Harchaoui, Sham Kakade, Vincent Roulet)
Large-scale optimization of structured prediction models
by inf-convolution smoothing of max-margin inference
We consider the problem of optimizing the parameters of a
maximum-margin structured prediction model at a large-scale.
We show how an appropriate smoothing by infimal convolutionof such non-smooth objectives paves the way to the development of large-scale smooth optimization algorithms making
calls to smooth inference oracles, comparable in complexity to
the regular inference oracles. We present such an algorithm
called Casimir, establish its worst-case information-based complexity, and demonstrate its effectiveness on several machinelearning applications.

Tue.4 16:30–17:45

The communication overhead is a key bottleneck that hinders perfect scalability of distributed optimization algorithms.
Various recent works proposed to use quantization or sparsification techniques to reduce the amount of data that needs to becommunicated. We analyze Stochastic Gradient Descent (SGD)with compression of all exchanges messages (like e.g. sparsification or quantization) and show that these schemes converge
at the same rate as vanilla SGD when equipped with error
compensation technique (i.e. keeping track of accumulated
errors in memory).

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VIS
Tue.4 H 0110
Semismoothness for Set-Valued Mappings and Newton
Method (Part I)
Organizer: Helmut Gfrerer
Chair: Helmut Gfrerer
Ebrahim Sarabi, Miami University, Ohio (joint work with Boris
Mordukhovich)
A Semismooth Inverse Mapping Theorem via Tilt Stability
and Its Applications in the Newton Method
We present a semismooth inverse mapping theorem for tiltstable local minimizers of a function. Then we discuss a Newton
method for a tilt-stable local minimum of an unconstrained
optimization problem.
Jiri Outrata, The Institute of Information Theory and Automation (joint work with Helmut Gfrerer)
On semismooth sets and mappings

Tue.4 16:30–17:45

The talk is devoted to a new notion of semismoothness
which pertains both sets as well as multifunctions. In the caseof single-valued maps it is closely related with the standard
notion of semismoothness introduced by Qi and Sun in 1993.
Semismoothness can be equivalently characterized in terms ofregular, limiting and directional limiting coderivatives and seems
to be the crucial tool in construction of Newton steps in case ofgeneralized equations. Some basic classes of semismooth sets
and mappings enabling us to create an elementary semismoothcalculus will be provided.
Helmut Gfrerer, Johannes Kepler University Linz (joint workwith Jiri Outrata)
On a semismooth Newton method for generalized equations
The talk deals with a semismooth Newton method for solving
generalized equations (GEs), where the linearization concerns
both the single-valued and the multi-valued part and it isperformed on the basis of the respective coderivatives. Twoconceptual algorithms will be presented and shown to converge
locally superlinearly under relatively weak assumptions. Thenthe second one will be used in order to derive an implementable
Newton-type method for a frequently arising class of GEs.

CON
Tue.4 H 2032
Advances in Polynomial Optimization
Organizer: Luis Zuluaga
Chair: Luis Zuluaga
Olga Kuryatnikova, Tilburg University (joint work with Renata
Sotirov, Juan Vera)
New SDP upper bounds for the maximum k-colorable subgraph problem
For a given graph with n vertices, we look for the largest
induced subgraph that can be colored in k colors such thatno two adjacent vertices have the same color. We propose
several new SDP upper bounds for this problem. The matrix
size in the basic SDP relaxations grows with k. To avoid thisgrowth, we use the invariance of the problem under the color
permutations. As a result, the matrix size reduces to order(n + 1), independently of k or the graph considered. Numericalexperiments show that our bounds are tighter than the existing
SDP and IP-based bounds for about a half of tested graphs.
Benjamin Peters, Otto von Guericke University Magdeburg
Convexification by means of monomial patterns
A novel convexification strategy based on relaxing monomial
relationships is presented. We embed both currently popular
strategies, general approaches rooted in nonlinear programming and approaches based on sum-of-squares or moment
relaxations, into a unified framework. Within this framework
we are able to trade off the quality of relaxation against computational expenses. Our computational experiments show thata combination with a prototype cutting-plane algorithm gives
very encouraging results.
Timo de Wolff, TU Braunschweig (joint work with Adam
Kurpisz)
New Dependencies of Hierarchies in Polynomial Optimization
We compare four key hierarchies for solving Constrained
Polynomial Optimization Problems (CPOP): Sum of Squares(SOS), Sum of Diagonally Dominant (SDSOS), Sum of Nonnegative Circuits (SONC), and the Sherali Adams (SA) hierarchies.
We prove a collection of dependencies among these hierarchies. In particular, we show on the Boolean hypercube that
Schmüdgen-like versions SDSOS*, SONC*, and SA* of thehierarchies are polynomially equivalent. Moreover, we showthat SA* is contained in any Schmüdgen-like hierarchy thatprovides a O(n) degree bound.

Parallel Sessions Abstracts Tue.4 16:30–17:45

Levent Tuncel, University of Waterloo (joint work with Lieven
Vandenberghe)
Recent progress in primal-dual algorithms for convex optimization
We will present new primal-dual algorithms for hyperbolic
programming problems. Our algorithms have many desired
properties including polynomial-time iteration-complexity forcomputing an approximate solution (for well-posed instances).
In the special case of dense SDPs (as well as for symmetric cone
programming problems with self-scaled barriers), our algorithms
specialize to Nesterov-Todd algorithm. However, for certain
sparse SDPs, our algorithms are new and have additional desired
properties.
Leonid Faybusovich, University of Notre Dame (joint work with
Cunlu Zhou)
Self-concordance and matrix monotonicity with applications to quantum entanglement problem
We construct a new class of objective functions on a symmetric cone compatible in the sense of Nesterov and Nemirovsky
with the standard self-concordant barrier attached to the symmetric cone. The construction involves a matrix monotone
scalar function on a positive semi-line. This allows us to develop
a long-step path-following algorithm for symmetric programming problems with both equality and inequality constraints
and objective functions from the class. In particular, the objective function arising in quantum entanglement problem belongs
to this class. Numerical results are presented.
Fatma Kilinc-Karzan, Carnegie Mellon University (joint workwith C.J. Argue)
On Sufficient and Necessary Conditions for Rank-1 Generatedness Property of Cones
A convex cone K that is a subset of the positive semidefinite(PSD) cone is rank-one generated (ROG) if all of its extreme
rays are generated by rank 1 matrices. ROG property is closely
related to the characterizations of exactness of SDP relaxations,e.g., of nonconvex quadratic programs. We consider the casewhen K is obtained as the intersection of the PSD cone withfinitely many linear (or conic) matrix inequalities, and identify
sufficient conditions that guarantee that K is ROG. In the caseof two linear matrix inequalities, we also establish the necessityof our sufficient condition.

CNV
Tue.4 H 1029
Quasi-Newton-Type Methods for Large-Scale Nonsmooth
Optimization
Organizers: Andre Milzarek, Zaiwen Wen
Chair: Andre Milzarek
Ewout van den Berg, IBM T.J. Watson
Speeding up basis-pursuit denoise: hybrid quasi-Newton
and beyond
The well-known basis-pursuit denoise problem can be solved
using the root-finding approach that was introduced in the
SPGL1 solver. In this talk we present several techniques that
can improve the performance of the solver. For the Lasso
subproblems we develop a hybrid quasi-Newton method thatenables light-weight switching between the regular spectral
projected-gradient steps and quasi-Newton steps restricted
to the current face of the one-norm ball. We also introduce
additional improvements to the root-finding procedure and thegeneration of dual-feasible points.
Minghan Yang, Peking University (joint work with Andre
Milzarek, Zaiwen Wen, Tong Zhang)
A stochastic quasi-Newton extragradient method for nonsmooth nonconvex optimization
We present a stochastic quasi-Newton extragradient method
for solving composite-type optimization problems. We assume
that gradient information of the smooth part of the objectivefunction can only be accessed via stochastic oracles. The proposed algorithm combines semismooth quasi-Newton steps
for a fixed-point equation with stochastic proximal extragradient steps and converges to stationary points in expectation.
Local convergence results are discussed for a variant of theapproach using variance reduction and numerical comparisonsare provided illustrating the efficiency of the method.
Florian Mannel, University of Graz (joint work with Armin
Rund)
A hybrid semismooth quasi-Newton method for large-scale
structured nonsmooth optimization
We present an algorithm for the efficient solution of structured nonsmooth operator equations in Banach spaces. The
term structured indicates that we consider equations which
are composed of a smooth and a semismooth mapping. The
novel algorithm combines a semismooth Newton method witha quasi-Newton method. This hybrid approach retains the local
superlinear convergence of both these methods under standard
assumptions and can be embedded in a matrix-free limitedmemory truncated trust-region framework. Numerical results
on nonconvex and nonsmooth large-scale real-world problems
are provided.

Tue.4 16:30–17:45

CON
Tue.4 H 3006
Geometry and Algorithms for Conic Optimization
Organizer: Fatma Kilinc-Karzan
Chair: Fatma Kilinc-Karzan

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CNV
Tue.4 H 3002
Performance Estimation of First-Order Methods
Organizers: François Glineur, Adrien Taylor
Chair: Adrien Taylor
Yoel Drori, Google (joint work with Ohad Shamir)
Performance estimation of stochastic first-order methods
We present an extension to the performance estimation
methodology, allowing for a computer-assisted analysis ofstochastic first-order methods. The approach is applicablein the convex and non-convex settings, and for various suboptimality criteria including the standard minimal gradient normof the iterates. We describe the approach, new numerical andanalytical bounds on the stochastic gradient method, and aconstruction of a worst-case example establishing tightness of
the analyses.
Donghwan Kim, KAIST
Accelerated Proximal Point Method for Maximally Monotone Operators
Tue.4 16:30–17:45

This work proposes an accelerated proximal point method
for maximally monotone operators. The proof is computerassisted via the performance estimation problem approach.
The proximal point method includes many well-known convex
optimization methods, such as the alternating direction methodof multipliers and the proximal method of multipliers, and thus
the proposed acceleration has wide applications. Numericalexperiments will be presented to demonstrate the accelerating
behaviors.
François Glineur, Université catholique de Louvain (joint workwith Antoine Daccache, Julien Hendrickx)
On the Worst-Case of the Fixed-Step Gradient Method
for Arbitrary Stepsizes
The problem of computing of the worst-case performanceof a given optimization algorithm over a given class of objective functions can in some cases be formulated exactly asa semidefinite optimization problem, whose solution provides
both the sought worst-case performance and a description thecorresponding objective function. We use this methodology
to study the gradient method performing N steps with fixed
but arbitrary stepsizes, applied to a smooth convex function.
Results suggest the existence of an exponential (in N) numberof distinct worst-case behaviours depending on the stepsizes.

DER
Tue.4 H 2038
Challenging Applications in Derivative-Free Optimization(Part II)
Organizers: Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Chair: Sébastien Le DigabelMark Abramson, Utah Valley University (joint work with Gavin
Smith)
Direct search applied to computation of penetration depth
between two polytopes arising from discretized geometry
modelsAn important consistency check in aerospace design is toensure that no two distinct parts occupy the same space. Discretization of geometry models often introduces small interferences not present in the fully detailed model. We focus on theefficient computation of penetration depth between two 3-D
convex polytopes, a well-studied computer graphics problem,
to distinguish between real interference and discretization error.
We reformulate a 5-variable constrained global optimization
problem into a 2-variable nonsmooth problem to more easilyfind global solutions with a direct search method.
Miguel Munoz Zuniga, IFPEN (joint work with Delphine Sinoquet)
Kriging based optimization of a complex simulator with
mixed variables: A randomized exploration of the categorical variables in the Expected Improvement suboptimization
Real industrial studies often boils down to complex optimization problems involving mixed variables and time consuming
simulators. To deal with these difficulties we propose the use ofa Gaussian process regression surrogate with a suitable kernel
able to capture simultaneously the output correlations withrespect to continuous and categorical/discrete inputs without
relaxation of the categorical variables. The surrogate is integrated in the Efficient Global Optimization method based on
the maximization of the Expected Improvement criterion. Thismaximization is a Mixed Integer Non-Linear problem which
we tackle with an adequate optimizer : the Mesh Adaptive
Direct Search, integrated in the NOMAD library. We introduce
a random exploration of the categorical space with a databased probability distribution and we illustrate the full strategy
accuracy on a toy problem. Finally we compare our approachwith other optimizers on a benchmark of functions.
Phillipe Rodrigues Sampaio, Veolia Research and Innovation(joint work with Stephane Couturier, Thomas Lecomte, Vincent
Martin, David Mouquet, Gabriela Naves Maschietto)
Optimization of district heating networks using a grey-box
MINLP approach
District heating networks (DHN) are a driving force fordecarbonization and energy efficiency. A DHN produces thermalenergy and delivers it over distribution networks to buildings.
We propose a production scheduling optimization tool thatconsiders heat production and distribution. We use mixedinteger nonlinear models where heat-only and cogeneration
units are handled. The network dynamics is addressed by ablack-box simulation model. We employ RBF models for timeseries forecasting that replace the black-box function to obtainan approximate solution. Results on a real case study are
shown.

Parallel Sessions Abstracts Tue.4 16:30–17:45

Daphne Skipper, United States Naval Academy (joint workwith Jon Lee, Emily Speakman)
Gaining and losing perspective
We discuss MINLO formulations of the disjunction x ∈{0} ∪ [l, u], where z is a binary indicator of x ∈ [l, u], and ycaptures the behavior of xp , for p > 1. This model is usefulwhen activities have operating ranges and we pay a fixed costfor carrying out each activity. Using volume as a measure tocompare convex bodies, we investigate a family of relaxations
for this model, employing the inequality yz q ≥ xp , parameterized by the “lifting exponent” q ∈ [0, p − 1]. We analyticallydetermine the behavior of these relaxations as functions ofl, u, p and q.

MUL
Tue.4 H 3013
Recent Developments in Set Optimization (Part IV)
Organizers: Akhtar A. Khan, Elisabeth Köbis, Christiane Tammer
Chair: Elisabeth Köbis
Radu Strugariu, Gheorghe Asachi Technical University of Iasi
(joint work with Marius Durea, Marian Pantiruc)
Stability of the directional regularity
We introduce a type of directional regularity for mappings,
making use of a special kind of minimal time function. We
devise a new directional Ekeland Variational Principle, which we
use to obtain necessary and sufficient conditions for directional
regularity, formulated in terms of generalized differentiation
objects. Stability of the directional regularity with respect tocompositions and sums is analyzed. Finally, we apply this study
to vector and scalar optimization problems with single andset-valued maps objectives.

Claudia D’Ambrosio, CNRS / École Polytechnique (joint workwith Jon Lee, Daphne Skipper, Dimitri Thomopulos)
Handling separable non-convexities with disjunctive cuts

Teodor Chelmus, Alexandru Ioan Cuza University of Iasi (joint
work with Marius Durea, Elena-Andreea Florea)
Directional Pareto efficiency

The Sequential Convex Mixed Integer Non Linear Programming (SC-MINLP) method is an algorithmic approach forhandling separable nonconvexities in the context of global optimization. The algorithm is based on a sequence of convex
MINLPs providing lower bounds on the optimal solution value.
Solving at each iteration a convex MINLP is the main bottleneck of SC-MINLP. We propose to improve this phase by
employing disjunctive cuts and solving instead a sequence of
convex NLPs. Computational results show the viability of ourapproach.

We study a notion of directional Pareto minimality thatgeneralizes the classical concept of Pareto efficiency. Then,considering several situations concerning the objective mapping
and the constraints, we give necessary and sufficient conditions
for the directional efficiency. We investigate different cases
and we propose some adaptations of well-known constructionsof generalized differentiation. In this way, the connections
with some recent directional regularities come into play. As
a consequence, several techniques from the study of genuine
Pareto minima are considered in our setting.

Leo Liberti, CNRS / École Polytechnique (joint work with
Claudia D’Ambrosio, Pierre-Louis Poirion, Ky Vu)
Random projections for quadratic optimization

Diana Maxim, Alexandru Ioan Cuza University of Iasi (joint
work with Elena-Andreea Florea)
Directional minimality concepts in vector optimization

Random projections are used as dimensional reduction techniques in many situations. They project a set of points in ahigh dimensional space to a lower dimensional one while approximately preserving all pairwise Euclidean distances. Usually,
random projections are applied to numerical data. In this talkwe present a successful application of random projections toquadratic programming problems. We derive approximate feasibility and optimality results for the lower dimensional problem,and showcase some computational experiments illustrating theusefulness of our techniques.

We study sufficient conditions for directional metric regularityof an epigraphical mapping in terms of generalized differentiation objects. The main feature proposed by our approachis the possibility to avoid the demanding assumption on theclosedness of the graph of such a mapping. To this aim, we
prove a version of Ekeland Variational Principle adapted to thesituation we consider. Then, we apply our directional openness
result to derive optimality conditions for directional Pareto
minimality.

Tue.4 16:30–17:45

GLO
Tue.4 H 1012
Techniques in Global Optimization
Organizers: Jon Lee, Emily Speakman
Chair: Jon Lee

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IMP
Tue.4 H 0111
Computational Optimization
Chair: Spyridon Pougkakiotis
Steven Diamond, Stanford University (joint work with Akshay
Agrawal)A Rewriting System for Convex Optimization Problems
We present a modular rewriting system for translating optimization problems written in a domain-specific language toforms compatible with low-level solver interfaces. Translation
is facilitated by reductions, which accept a category of problems and transform instances of that category to equivalentinstances of another category. The proposed system is implemented in version 1.0 of CVXPY, a widely used domain-specificlanguage for convex optimization. We illustrate how the system facilitated CVXPY’s extension to disciplined geometricprogramming, a generalization of geometric programming.
Spyridon Pougkakiotis, University of Edinburgh (joint workwith Jacek Gondzio, Kresimir Mihic, John Pearson)
Exploiting Circulant Properties of Matrices in Structured
Optimization Problems
Tue.4 16:30–17:45

Circulant matrices are prevalent in many areas of mathematics. Their eigenvalue decomposition can be computed
expeditiously, using the Fast Fourier Transform (FFT). In thistalk, we discuss advantages of exploiting circulant propertiesof matrices arising in certain structured optimization problems.
Harun Aydilek, Gulf University for Science and Technology(joint work with Ali Allahverdi, Asiye Aydilek)[moved] Minimizing Total Tardiness in a No-Wait Flowshop Scheduling Problem with Separate Setup Times withbounded MakespanNo-wait flowshop scheduling problem is addressed to minimize total tardiness where the Cmax is bounded. A new simulated annealing algorithm (NSA) utilizing a block insertionand exchange operators is proposed. NSA is combined with an
iterated search where the block SA explores the search space
for a smaller total tardiness while the iterated search satisfies
the constraint, called PA. PA is compared with 6 closely related and best algorithms. Computational experiments basedon simulations reveal that PA reduces the error of the bestalgorithm by 50 %. The results are statistically tested.

PDE
Tue.4 H 0106
Computational Design Optimization (Part IV)
Organizers: Martin Siebenborn, Kathrin Welker
Chair: Kathrin Welker
Martin Berggren, Umeå Universitet (joint work with Anders
Bernland, André Massing, Eddie Wadbro)
3D acoustic shape optimization including visco-thermalboundary-layer losses
When applying shape optimization to design certain acoustic
devices, a challenge is to account for effects of the viscothermal boundary layers that appear in narrow channels andcavities. An accurate model of this phenomenon uses a socalled Wentzell boundary condition together with the Helmholtz
equation for the acoustic pressure. For the first time, this modelis here used to successfully design such a device, a compression
driver feeding a loudspeaker horn. The computations make useof a level-set description of the geometry and the CutFEM
framework implemented in the FEniCS platform.
Martin Siebenborn, Universität Hamburg (joint work with Andreas Vogel)A shape optimization algorithm for cellular composites
In this talk we present a mesh deformation technique for
PDE constrained shape optimization. Introducing a gradientpenalization to the inner product for linearized shape spaces,
mesh degeneration can be prevented and the scalability of
employed solvers can be retained. We illustrate the approachby a shape optimization for cellular composites with respect
to linear elastic energy under tension. The influence of thegradient penalization is evaluated and the parallel scalability of
the approach demonstrated employing a geometric multigrid
solver on hierarchically distributed meshes.
Christian Vollmann, Trier University (joint work with Volker
Schulz)Shape optimization for interface identification in nonlocal
modelsShape optimization has been proven useful for identifying
interfaces in models governed by partial differential equations.
For instance, shape calculus can be exploited for parameter
identification of problems where the diffusivity is structuredby piecewise constant patches. On the other hand, nonlocal
models, which are governed by integral operators instead ofdifferential operators, have attracted increased attention inrecent years. We bring together these two fields by consideringa shape optimization problem which is constrained by a nonlocal
convection-diffusion equation.

Parallel Sessions Abstracts Tue.4 16:30–17:45

Maria Soledad Aronna, Fundação Getúlio Vargas (joint workwith Monica Motta, Franco Rampazzo)
A higher-order maximum principle for impulsive optimal
control problem
We consider a nonlinear control system, affine with respect
to an unbounded control u taking values in a closed cone of
Rm , and with drift depending on a second, ordinary control a,ranging inn a bounded set. We provide first and higher ordernecessary optimality conditions for a Bolza problem associatedto this system. We illustrate how the chance of using impulse
perturbations, makes it possible to derive a Maximum Principle
which includes both the usual maximum condition and higher
order conditions involving Lie brackets of drift and non-drift
vector fields
Jorge Becerril, Universidade do Porto
On optimal control problems with irregular mixed constraints
First order necessary conditions for optimal control problems with mixed constraints are well-known when a regularity
criterion is satisfied. On the contrary, problems involving nonregular mixed constraints have received little attention, except
for some isolated works, despite being relevant in many areas
such as robotics and problems involving sweeping systems. We
present necessary conditions for problems involving nonregular
mixed constraints derived via infinite dimensional optimization.
Moreover, we present a form of regularity which leads to theusual first order necessary conditions.
Michele Palladino, Gran Sasso Science InstituteHandling Uncertainty in Optimal Control
The talk discusses one possible model problem for certain
tasks in reinforcement learning. The model provides a framework to deal with situations in which the system dynamics are
not known and encodes the available information about thestate dynamics as a measure on the space of functions. Sucha measure updates in time, taking into account all the previous measurements and extracting new information from them.
Technical results about the value function of such a problem
are stated, and several possible applications to reinforcement
learning and the exploration-exploitation tradeoff are discussed.

PDE
Tue.4 H 0112
Fractional/Nonlocal PDEs: Applications, Control, and Beyond (Part I)
Organizers: Harbir Antil, Carlos Rautenberg, Mahamadii
Warma
Chair: Harbir Antil
Ekkehard Sachs, Universität Trier
Economic Models with Nonlocal Operators
The Ramsey model is one of the most popular neoclassicalgrowth models in economics. The basic time-dependent model
has been extended by a spatial component in recent years, i.e.
capital accumulation is modeled as a process not only in time
but in space as well. We consider a Ramsey economy wherethe value of capital depends not only on the respective location
but is influenced by the surrounding areas as well. Here, we
do not only take spatially local effects modeled by a diffusion
operator into account but we also include a nonlocal diffusion
term in integral form leading to control PIDEs.
Mahamadii Warma, University of Puerto Rico, Rio Piedras
CampusControllability of fractional heat equations under positivity
constraints
We analyze the controllability properties under positivity constraints of the heat equation involving the fractional Laplacianon the interval (−1, 1). We prove the existence of a minimal(strictly positive) time Tmin such that the fractional heat dynamics can be controlled from any non-negative initial datum
to a positive steady state, when s > 1/2. Moreover, we provethat in this minimal time constrained, controllability can also
be achieved through a control that belongs to a certain spaceof Radon measures. We also give some numerical results.
Carlos Rautenberg, Humboldt-Universität zu Berlin / WIAS
A nonlocal variational model in image processing associated to the spatially variable fractional Laplacian
We propose a new variational model in weighted Sobolev
spaces with non-standard weights and applications to image
processing. We show that these weights are, in general, not of
Muckenhoupt type and therefore the classical analysis tools
may not apply. For special cases of the weights, the resulting
variational problem is known to be equivalent to the fractional
Poisson problem. We propose a finite element scheme to solve
the Euler-Lagrange equations, and for the image denoising
application we propose an algorithm to identify the unknown
weights. The approach is illustrated on several test problems
and it yields better results when compared to the existing total
variation techniques.

Tue.4 16:30–17:45

PDE
Tue.4 H 0107
Optimal Control and Dynamical Systems (Part IV)
Organizers: Cristopher Hermosilla, Michele Palladino
Chair: Cristopher Hermosilla

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Tue.4 16:30–17:45

ROB
Tue.4 H 1058
Recent Advances in Distributionally Robust Optimization
Organizer: Peyman Mohajerin Esfahani
Chair: Peyman Mohajerin Esfahani

Tue.4 H 2053
Robust Optimization: Theory and Applications
Organizer: Vineet Goyal
Chair: Vineet Goyal

ROB

Jose Blanchet, Stanford University (joint work with Peter Glynn,
Zhengqing Zhou)
Distributionally Robust Performance Analysis with Martingale Constraints

Chaithanya Bandi, Kellogg School of Management, Northwestern University
Using RO for Simulation Optimization using the Methodof Types

We consider distributionally robust performance analysis ofpath-dependent expectations over a distributional uncertainty
region which includes both a Wasserstein ball around a benchmark model, as well as martingale constraints. These typesof constraints arise naturally in the context of dynamic optimization. We show that these problems (which are infinite
dimensional in nature) can be approximated with a canonicalsample complexity (i.e. square root in the number of samples).
We also provide various statistical guarantees (also in line withcanonical statistical rates).

The method of types is one of the key technical tools in
Information Theory with applications to Shannon theory, hypothesis testing and large deviations theory. In this talk, we
present results using this approach to construct generalized
typical sets that allow us analyze and optimize the expected behavior of stochastic systems. We then use the idea of Averaged
RO approach developed by Bertsimas and Youssef (2019) toapproximate and optimize the expected behavior. We illustrate
our approach by finding optimal control policies for multi-classqueueing networks arising in data centers.

John Duchi, Stanford University (joint work with Hongseok
Namkoong)
Distributional Robustness, Uniform Predictions, and applications in Machine Learning

Daniel Zhuoyu Long, CUHK (joint work with Jin Qi, Aiqi
Zhang)The Supermodularity in Two-Stage Distributionally Robust Optimization

A common goal in statistics and machine learning is to learn
models that can perform well against distributional shifts, suchas latent heterogeneous subpopulations, unknown covariate
shifts, or unmodeled temporal effects. We develop and analyze
a distributionally robust stochastic optimization frameworkthat learns a model that provides good performance against
perturbations to the data-generating distribution. We give aconvex optimization formulation for the problem, providing
several convergence guarantees, including finite sample upperand lower minimax bounds.

We solve a class of two-stage distributionally robust optimization problems which are with the property of supermodular.
We apply the monotone coupling results and derive the worstcase distribution precisely, which leads to the tractability ofthis two-stage distributionally robust optimization problem.
Furthermore, we provide a necessary and sufficient condition
to check if any given problem has supermodular property. We
apply this framework to classical problems including multiitem newsvendor problems, general assemble-to-order (ATO)
systems and the appointment scheduling problem.

Peyman Mohajerin Esfahani, TU Delft (joint work with DanielKuhn, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh)
Wasserstein Distributionally Robust Optimization: Theory
and Applications in Machine Learning

Vineet Goyal, Columbia University (joint work with Omar El
Housni)Beyond Worst Case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization

In this talk, we review recent developments in the area of
distributionally robust optimization, with particular emphasis
on a data-driven setting and robustness to the so-called Wasserstein ambiguity set. We argue that this approach has several
conceptual and computational benefits. We will also show that
Wasserstein distributionally robust optimization has interesting
ramifications for statistical learning and motivates new approaches for fundamental learning tasks such as classification,
regression, maximum likelihood estimation or minimum meansquare error estimation, among others.

Affine policies are widely used in dynamic optimization where
computing an optimal adjustable solution is often intractable.
While the worst case performance of affine policies is significantly bad, the observed empirical performance is near-optimal
for a large class of instances. In this paper, we aim to address
this stark-contrast between the worst-case and the empiricalperformance of affine policies, and show that affine policies
give a provably good approximation for two-stage robust optimization problem with high probability on random instances
for a large class of distributions.

Parallel Sessions Abstracts Tue.4 16:30–17:45

105

ROB
Tue.4 H 3012
Embracing Extra Information in Distributionally Robust
ModelsOrganizer: Rui Gao
Chair: Rui Gao

SPA
Tue.4 H 2033
Sparse Optimization and Information Processing - Contributed Session I
Organizer: Shoham Sabach
Chair: Jordan Ninin

Zhenzhen Yan, Nanyang Technological University (joint workwith Louis Chen, Will Ma, Karthik Natarajan, David SimchiLevi)
Distributionally Robust Linear and Discrete Optimization
with Marginals

Nikolaos Ploskas, University of Western Macedonia (joint workwith Nikolaos Sahinidis, Nikolaos Samaras)
An advanced initialization procedure for the simplex algorithm

Mathias Pohl, University of Vienna
Robust risk aggregation with neural networks
We consider settings in which the distribution of a multivariate random variable is partly ambiguous: The ambiguity lies on
the level of dependence structure, and that the marginal distributions are known. We work with the set of distributions thatare both close to a given reference measure in a transportation
distance and additionally have the correct marginal structure.
The goal is to find upper and lower bounds for integrals ofinterest with respect to distributions in this set. The described
problem appears naturally in the context of risk aggregation.
Zhiping Chen, Xi’an Jiaotong University
Stability analysis of optimization problems with distributionally robust dominance constraints induced by full random recourse
We derive the quantitative stability results, under the appropriate pseudo metric, for stochastic optimization problems with
distributionally robust dominance constraints induced by full
random recourse. To this end, we first establish the qualitativeand quantitative stability results of the optimal value function
and the optimal solution set for optimization problem with k-thorder stochastic dominance constraints under the Hausdorff
metric, which extend the present results to the locally Lipschitz
continuity case.

This paper addresses the computation of an initial basis
for the simplex algorithm for linear programming. We propose
six algorithms for constructing an initial basis that is sparseand will reduce the fill-in and computational effort during LU
factorization and updates. Over a set of 62 large benchmarks,
the best proposed algorithm produces remarkably sparse starting bases. Taking into account only the hard instances, theproposed algorithm results in 23% and 30% reduction of thegeometric mean of the execution time of CPLEX’s primal anddual simplex algorithm, respectively.
Jordan Ninin, ENSTA-Bretagne (joint work with Ramzi Ben
Mhenni, Sebastien Bourguignon)
Global Optimization for Sparse Solution of Least Squares
Problems
Finding low-cardinality solutions to least-squares problems
has found many applications in signal processing. We propose a branch-and-bound algorithm for global optimization ofcardinality-constrained and cardinality-penalized least-squares,
and cardinality minimization under quadratic constraints. A
tree-search exploration strategy is built based on forward selection heuristics, and the continuous relaxation problems are
solved with a dedicated algorithm inspired by homotopic methods. On all conducted experiments, our algorithms at least
equal and often outperform the MIP solver CPLEX.
Montaz Ali, WITS (joint work with Ahdam Stoltz)
Call Center’s Optimization Problem
The special and unique features of this problem are thenshown to be: the need to perform scheduling on a batchbasis every day, without knowledge of what will occur thefollowing day and also the requirement that resources (callcentre capacity) are fully utilized and not exceeded. The resultof the research is that a new mathematical model called the
Critical Time Window Resource Levelling Problem (CTWRLP)
with the Continual Rescheduling method is developed andproposed as a method for solving the real world schedulingproblem.

Tue.4 16:30–17:45

In this paper, we study the class of linear and discrete optimization problems in which the objective coefficients arechosen randomly from a distribution, and the goal is to evaluate robust bounds on the expected optimal value as well as
the marginal distribution of the optimal solution. The set of
joint distributions is assumed to be specified up to only themarginal distributions. Though the problem is NP-Hard, we establish a primal-dual analysis that yields sufficiency conditions
for poly-time solvability as well as computational procedures.
Application problems are also explored.

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SPA
Tue.4 H 3007
Nonlinear Optimization Algorithms and Applications in
Data Analysis (Part III)
Organizers: Xin Liu, Cong Sun
Chair: Cong Sun
Geovani Grapiglia, Universidade Federal do Paraná (joint workwith Yurii Nesterov)
Regularized Tensor Methods for Minimizing Functions
with Hölder Continuous Higher-Order Derivatives
In this work, we propose p-order methods for minimization of convex functions that are p-times differentiable with
ν-Hölder continuous p-th derivatives. For the schemes without acceleration, we establish iteration complexity bounds ofO(−1/(p+ν−1) ). Assuming that ν is known, we obtain an improved complexity bound of O(−1/(p+ν) ) for the corresponding
accelerated scheme. For the case in which ν is unknown, we
present a universal accelerated tensor scheme with complexity of O(−p/(p+1)(p+ν−1) ). A lower complexity bound is also
obtained.
Yadan Chen, Academy of Mathematics and System Science,
Chinese Academy of Sciences (joint work with Xin Liu, Yaxiang
Yuan)
Alternating Direction Method of Multipliers to Solve Two
Continuous Models for Graph-based Clustering

Wed.1 11:30–12:45

Clustering is an important topic in data science. The popular
approaches including K-means algorithm and nonnegative
matrix factorization (NMF) directly use data points as input
and consider problems in Euclidean space. In this paper, we
propose a new approach which considers Gaussian kernel basedon graph construction. Moreover, the final cluster labels can bedirectly obtained without post-processing. The experimental
results on real data sets have shown that the proposed approachachieves better clustering results in terms of accuracy andmutual information.

Parallel Sessions Abstracts Wed.1 11:30–12:45

107

Wed.1 11:30–12:45
APP

APP
Wed.1 H 3025
Mathematical Optimization in Signal Processing (Part I)
Organizers: Ya-Feng Liu, Zhi-Quan Luo
Chair: Zhi-Quan Luo

Jacek Gondzio, The University of Edinburgh (joint work with
Alemseged Gebrehiwot Weldeyesus)
Computational Challenges in Structural Optimization

Sergiy A. Vorobyov, Aalto University (joint work with Mihai I.
Florea)Accelerated majorization based optimization for largescale signal processing: Some new theoretical concepts
and applications

We address challenges related to solving real world problems
in structural optimization. Such problems arise for example
in layout optimization of tall buildings, long bridges or large
spanning roofs. We propose to solve such problems with a highly
specialized primal-dual interior point method. The problems
may be formulated as (very large) linear or, if additional stability
concerns are addressed, semidefinite programs. In this talk wewill focus on computational challenges arising in such problems
and efficient ways to overcome them.
Alexander Brune, University of Birmingham (joint work withMichal Kočvara)
Modified Barrier Multigrid Methods for Topology Optimization
One of the challenges of topology optimization lies in the sizeof the problems, which easily involve millions of variables. We
consider the example of the variable thickness sheet (VTS) problem and propose a Penalty-Barrier Multiplier (PBM) method
to solve it. In each iteration, we minimize a generalized augmented Lagrangian by the Newton method. The arising large
linear systems are reduced to systems suitable for a standard
multigrid method. We apply a multigrid preconditioned MINRES method. The algorithm is compared with the optimality
criteria (OC) method and an interior point (IP) method.
Tamás Terlaky, Lehigh University, Bethlehem, PA (joint workwith Ramin Fakhimi, Sicheng He, Weiming Lei, Joaquim Martins, Mohammad Shahabsafa, Luis Zuluaga)
Truss topology design and sizing optimization with kinematic stability
We propose a novel mathematical optimization model for
Truss Topology Design and Sizing Optimization (TTDSO)
problem with Euler buckling constraints. We prove that the
proposed model, with probability 1, is delivering a kinematicly
stable structure. Computational experiments, using an adaptation of our MILO-moving neighborhood search strategy, showsthat our model and computational approach is superior whencompared to other approaches.

Numerous problems in signal processing are cast or approximated as large-scale convex optimization problems. Becauseof their size, such problems can be solved only using first-ordermethods. Acceleration is then becomes the main feature of
the methods. We will overview some our recent results on the
topic (the majorization minimization framework appears to be
an umbrella for many such methods including Nesterov’s FGM,FISTA, our ACGM, and others) and give a number of successful
applications in signal processing (LASSO, NNLS, L1LR, RR,
EN, radar waveform design with required properties).
Yang Yang, Fraunhofer ITWM (joint work with Marius Pesavento)
Parallel best-response algorithms for nonsmooth nonconvex optimization with applications in matrix factorizationand network anomaly detection
Block coordinate descent algorithms based on nonlinear bestresponse, or Gauss-Seidel algorithm, is one of the most popularalgorithm in large-scale nonsmooth optimization. In this paper, we propose a parallel (Jacobi) best-response algorithm,
where a stepsize is introduced to guarantee the convergence.
Although the original problem is nonsmooth, we propose a
simple line search method carried out over a properly designed
smooth function. The proposed algorithm, evaluated for thenonsmooth nonconvex sparsity-regularized rank minimization
problem, exhibts fast convergence and low complexity.
Bo Jiang, Nanjing Normal University (joint work with Shiqian
Ma, Anthony Man-Cho So, Shuzhong Zhang)
Vector Transport-Free SVRG with General Retraction for
Riemannian Optimization: Complexity Analysis and Practical Implementation
We propose a vector transport-free stochastic variance reduced gradient (TF-SVRG) method for empirical risk minimization over Riemannian manifold. The TF-R-SVRG we propose
handles general retraction operations, and does not need additional vector transport evaluations as done by most existing
manifold SVRG methods. We analyze the iteration complexityof TF-R-SVRG for obtaining an -stationary point and its local
linear convergence by assuming the Lojasiewicz inequality. We
also incorporate the Barzilai-Borwein step size and design avery practical TF-R-SVRG-BB method.

Wed.1 11:30–12:45

Wed.1 H 3004
Structural Design Optimization (Part I)
Organizer: Tamás Terlaky
Chair: Tamás Terlaky

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BIG
Wed.1 H 0104
Distributed Algorithms for Constrained Nonconvex Optimization: Clustering, Network Flows, and Two-Level Algorithms
Organizers: Andy Sun, Wotao Yin
Chair: Wotao Yin
Kaizhao Sun, Georgia Institute of Technology (joint work with
Andy Sun)
A two-level distributed algorithm for constrained nonconvex programs with global convergenceBIG
Wed.1 H 0110
Modern Optimization Methods in Machine Learning
Organizer: Qingna Li
Chair: Qingna Li
Chengjing Wang, Southwest Jiaotong University (joint workwith Defeng Sun, Peipei Tang, Kim-Chuan Toh)
A sparse semismooth Newton based proximal
majorization-minimization algorithm for nonconvex
square-root-loss regression problems

Wed.1 11:30–12:45

This work is motivated by the desire to solve network constrained nonconvex programs with convergence guarantees. We
propose a two-level algorithm with an inner level of ADMM in
an augmented Lagrangian framework that can guarantee global
convergence for a wide range of smooth and nonsmooth nonconvex constrained programs. We demonstrate the effectiveness of
the algorithm with comparison to existing algorithms on some
important classes of problems and discuss its convergence rate
properties.

In this paper, we consider high-dimensional nonconvex squareroot-loss regression problems. We shall introduce a proximal
majorization-minimization (PMM) algorithm for these problems. Our key idea for making the proposed PMM to be efficientis to develop a sparse semismooth Newton method to solve thecorresponding subproblems. By using the Kurdyka-Lojasiewicz
property exhibited in the underlining problems, we prove that
the PMM algorithm converges to a d-stationarity point. Extensive numerical experiments are presented to demonstrate
the high efficiency of the proposed PMM algorithm.

Tsung-Hui Chang, The Chinese University of Hong Kong, Shenzhen
Large-Scale Clustering by Distributed Orthogonally Constrained NMF Model

Peipei Tang, Zhejiang University City College (joint work with
Dunbiao Niu, Enbin Song, Chengjing Wang, Qingsong Wang)
A semismooth Newton based augmented Lagrangian
method for solving the support vector machine problems

The non-negative matrix factorization (NMF) model withan additional orthogonality constraint on one of the factor
matrices, called the orthogonal NMF (ONMF), has been foundto provide improved clustering performance over the K-means.
We propose an equivalent problem reformulation that transforms the orthogonality constraint into a set of norm-based
non-convex equality constraints. We then apply one smooth and
one non-smooth penalty approach to handle these non-convex
constraints, which result in desirable separable structures for
efficient parallel computations.

In this talk, we propose a highly efficient semismooth Newton
based augmented Lagrangian method for solving a large-scaleconvex quadratic programming problem generated by the dualof the SVM with constraints consisting of one linear equality
constraint and a simple box constraint. By leveraging on available error bound results to realize the asymptotic superlinear
convergence property of the augmented Lagrangian method,and by exploiting the second-order sparsity of the problem
through the semismooth Newton method, the algorithm we
propose can efficiently solve these difficult problems.

Tao Jiang, University of Waterloo (joint work with Stephen
Vavasis, Chen Wen (Sabrina) Zhai)
Recovery of a mixture of Gaussians by sum-of-norms clustering

Qingna Li, Beijing Institute of Technology (joint work with
Juan Yin)
A Semismooth Newton Method for Support Vector Classification and Regression

Sum-of-norms clustering is a novel clustering method using
convex optimization. Recently, Panahi et al. proved that sumof norms clustering is guaranteed to recover a mixture ofGaussians under the restriction that the sample size is not
too large. This paper lifts the restriction and shows that sumof-norms clustering with equal weights recovers a mixture ofGaussians even as the number of samples tends to infinity.
Our proof relies on an interesting characterization of clusters
computed by sum-of-norms clustering developed inside a proofof the agglomeration conjecture by Chiquet et al.

SVM is an important and fundamental technique in machine learning. In this talk, we apply a semismooth Newton
method to solve two typical SVM models: the L2 -loss SVC
model and the -L2 -loss SVR model. A common belief on thesemismooth Newton method is its fast convergence rate as wellas high computational complexity. Our contribution is that by
exploring the sparse structure of the models, we significantly
reduce the computational complexity, meanwhile keeping thequadratic convergence rate. Extensive numerical experiments
demonstrate the outstanding performance of the method.

Parallel Sessions Abstracts Wed.1 11:30–12:45

Gayatri Pany, Singapore University of Technology and Design
A variational inequality approach to model green transport
network
We aim to develop a transport network model that maximizes societal benefits and minimizes environmental effects interms of energy use and GHG emissions. The model pertains
to the use of different modes of transport, like automatedvehicles, electric vehicles. Based on different modes of travel, amulti-criteria transport model will be formulated. The proposed
approach will be to derive the optimality and the equilibrium
conditions, which will satisfy a variational inequality. The equilibrium will be characterized by analyzing the continuity andstrict monotonicity of the underlying operator.
Pankaj Gautam, Indian Institute of Technology (BHU)
Varanasi (joint work with Tanmoy Som Tanmoy)
Common zero of lower semicontinuous function and monotone operator
In this talk, we consider the problem to find the common
zero of a non-negative lower semicontinuous function and amaximal monotone operator in a real Hilbert space. Algorithms
permit us to solve the problem when the operators are not the
projection operator, or the projection is not easy to compute. Asan application, the result presented is used to study the strong
convergence theorem for split equality variational problem.
Some numerical examples are included to demonstrate theefficiency and applicability of the method.
Felipe Lara, Getulio Vargas Foundation
Existence results for equilibrium problems and mixed variational inequalities
We use asymptotic analysis for studying noncoercive pseudomonotone equilibrium problems and noncoercive mixed variational inequalities, both in the nonconvex case. We provide
general necessary and sufficient optimality conditions for theset of solutions to be nonempty and compact for both problems.
As a consequence, a characterization of the nonemptiness andcompactness of the solution sets is given. Finally, a comparison between our results with previous ones presented in theliterature is given.
This talk is based on two papers in collaboration with Alfredo
Iusem from IMPA, Brazil.

VIS
Wed.1 H 3013
Semismoothness for Set-Valued Mappings and Newton
Method (Part II)
Organizer: Helmut Gfrerer
Chair: Helmut Gfrerer
Martin Brokate, TU München
Sensitivity of rate-independent evolutions
Rate independent evolutions are inherently nonsmooth. In
this talk we present results concerning weak differentiability
properties of such evolutions resp. the solution operator of
the associated variational inequality. In particular, we discuss
whether these operators are semismooth.
Nico Strasdat, TU Dresden (joint work with Andreas Fischer)
A Newton-type method for the solution of complementarity problems
Recently, a Newton-type method for the solution of KarushKuhn-Tucker system has been developed, that builds on thereformulation of the system as system of nonlinear equations
by means of the Fischer-Burmeister function. Based on a similar idea, we propose a constrained Levenberg-Marquardt-type
method for solving a general nonlinear complementarity problem. We show that this method is locally superlinear convergent
under certain conditions which allow nonisolated and degenerate solutions.
Anna Walter, TU Darmstadt (joint work with Stefan Ulbrich)Numerical Solution Strategies for the Elastoplasticity
Problem with Finite Deformations
We consider the optimal control of elastoplasticity problems
with large deformations motivated by engineering applications.
To solve the optimal control problem, we need to handle theoccurring flow rule within the simulation. Therefore we analyze
different solution approaches which are motivated by methods
applied in linear elastoplasticity but extended to the case ofa multiplicative split of the deformation gradient. Due to thehigh complexity of the resulting model, we include reducedorder models to speed up the simulation process. We present
theoretical as well as computational aspects.

Wed.1 11:30–12:45

VIS
Wed.1 H 3008
Complementarity and Variational Inequalities - Contributed Session I
Chair: Felipe Lara

109

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Parallel Sessions Abstracts Wed.1 11:30–12:45

CON
Wed.1 H 0107
Semidefinite Approaches to Combinatorial Optimization
Organizer: Renata Sotirov
Chair: Renata Sotirov

Wed.1 H 2032
Polynomial Optimization (Part I)
Organizer: Grigoriy Blekherman
Chair: Grigoriy Blekherman

Hao Hu, Tilburg University (joint work with Renata Sotirov,
Henry Wolkowicz)
Combining symmetry reduction and facial reduction for
semidefinite programming

Oguzhan Yuruk, TU Braunschweig (joint work with E. Feliu,
N. Kaihnsa, B. Sturmfels, Timo de Wolff)
An analysis of multistationarity in n-site phosphorylation
cycle using circuit polynomials

For specially structured SDP instances, symmetry reduction isan application of representation theory to reduce the dimensionof the feasible set. For an SDP instance that fails Slater’s
condition, facial reduction technique can be employed to findthe smallest face of the PSD cone containing the feasible
region. The resulting SDP instance is of smaller dimension andsatisfies Slater’s condition. In this talk, we show that symmetry
reduction and facial reduction can be exploited at the sametime. Our method is simple to implement, and is applicable fora wide variety of applications of SDPs.

Parameterized ordinary differential equation systems are
crucial for modeling in biochemical reaction networks under
the assumption of mass-action kinetics. Various questions
concerning the signs of multivariate polynomials in positive
orthant arise from studying the solutions’ qualitative behavior
with respect to parameter values. In this work, we utilize circuit
polynomials to find symbolic certificates of nonnegativity toprovide further insight into the number of positive steady statesof the n-site phosphorylation cycle model.

Christian Truden, Alpen-Adria-Universität Klagenfurt (joint
work with Franz Rendl, Renata Sotirov)
Lower Bounds for the Bandwidth Problem

CON

João Gouveia, University of Coimbra (joint work with Mina
Saee Bostanabad, Alexander Kovacec)
Matrices of bounded factor width and sums of k-nomial
squares

Wed.1 11:30–12:45

The bandwidth problem asks for a simultaneous permutationof the rows and columns of the adjacency matrix of a graphsuch that all nonzero entries are as close as possible to the
diagonal. We present novel approaches to obtain lower bounds
on the bandwidth problem and introduce a vertex partition
problem to bound the bandwidth of a graph. By varying sizesof partitions, we have a trade-off between quality of bounds
and efficiency of computing them. To compute lower bounds,
we derive several SDP relaxations. Finally, we evaluate ourapproach on a carefully selected set of benchmark instances.

In 2004, Boman et al introduced the concept of factor widthof a semidefinite matrix A. This is the smallest k for which
one can write the matrix as A = V V T with each column of V
containing at most k non-zeros. In the polynomial optimization
context, these matrices can be used to check if a polynomial isa sum of squares of polynomials of support at most k. We will
prove some results on the geometry of the cones of matrices
with bounded factor widths and theirDaniel Brosch, Tilburg University (joint work with Etienne deKlerk)A new look at symmetry reduction of semidefinite relaxations of the quadratic assignment problem

I will discuss several recent results on symmetric nonnegative
polynomials and their approximations by sums of squares. I
will consider several types of symmetry, but the situation isespecially interesting in the limit as the number of variables
tends to infinity. There are diverse applications to quantum entanglement, graph density inequalities and theoretical computerscience.

The Jordan reduction, a symmetry reduction method forsemidefinite programming, was recently introduced for symmetric cones by Parrilo and Permenter. We extend this method tothe doubly nonnegative cone and investigate its application toa strong relaxation of the quadratic assignment problem. This
reduction is then used to efficiently calculate better bounds forcertain discrete energy minimization problems, which initiallyhave the form of semidefinite programs too large to be solved
directly.

Grigoriy Blekherman, Georgia Institute of Technology
Symmetry and Nonnegativity

Parallel Sessions Abstracts Wed.1 11:30–12:45

Bang Cong Vu, École Polytechnique Fédérale de Lausanne(EPFL)
A splitting method for three operators involving Lipschitzian operators
We investigate the problem of finding a zero point of the sumof 3 operators where one of them is monotone Lipschitzian. We
propose a new splitting method for solving this inclusion in real
Hilbert spaces. The proposed framework unifies several splitting method existing in the literature. The weak convergenceof the iterates is proved. The strong convergence is also obtained under an additional assumptions. Applications to sumsof composite monotone inclusions and composite minimization
problems are demonstrated.
Saverio Salzo, Istituto Italiano di Tecnologia
Parallel random block-coordinate forward-backward algorithm: A complete convergence analysis
We study the block coordinate forward-backward algorithm
where the blocks are updated in a random and possibly parallelmanner. We consider the convex case and provide a unifying analysis of the convergence under different hypotheses,
advancing the state of the art in several aspects.
Patrick L. Combettes, North Carolina State University (joint
work with Minh N. Bui)
A general Bregman-based splitting scheme for monotone
inclusions
We present a unifying framework for solving monotone inclusions in general Banach spaces using Bregman distances.
This is achieved by introducing a new property that includes
in particular the standard cocoercivity property. Several classical and recent algorithms are featured as special cases of ourframework. The results are new even in standard Euclidean
spaces. Applications are discussed as well.

CNV
Wed.1 H 1058
Convex and Nonsmooth Optimization - Contributed Session I
Chair: Vladimir Shikhman
Igor Konnov, Kazan Federal University
Gradient Methods with Regularization for Optimization
Problems in Hilbert spaces
We suggest simple implementable modifications of gradienttype methods for smooth convex optimization problems in
Hilbert spaces, which may be ill-posed. At each iteration, theselected method is applied to some perturbed optimization
problem, but we change the perturbation only after satisfying
a simple estimate inequality, which allows us to utilize mild
rules for the choice of the parameters. Within these rules weprove strong convergence and establish complexity estimates
for the methods. Preliminary results of computational tests
confirm efficiency of the proposed modification.
Ali Dorostkar, Isfahan Mathematics House (IMH)An Introduction to Fractional Dimensional Optimization
ProblemA general framework for fractional dimensional optimization
method is introduced. It is shown that there is a tangent line offunction in fractional dimensions which directly passes throughthe root. The method is presented to investigate a possibilityof direct solution of optimization problem, however; analysis of
fractional dimension needs further investigation. Beside method
has a possibility to use for convex and non-convex and also
local and global optimization problems. As a point, fractional
dimensional optimization method opens a different view anddirection for optimization problem.
Vladimir Shikhman, Chemnitz University of Technology (joint
work with David Müller, Yurii Nesterov)
Discrete choice prox-functions on the simplex
We derive new prox-functions on the simplex from additive
random utility models of discrete choice. They are convex conjugates of the corresponding surplus functions. We explicitly
derive the convexity parameter of discrete choice prox-functions
associated with generalized extreme value models, and specifically with generalized nested logit models. Incorporated into
subgradient schemes, discrete choice prox-functions lead tonatural probabilistic interpretations of the iteration steps. As illustration we discuss an economic application of discrete choiceprox-functions in consumption cycle.

Wed.1 11:30–12:45

CNV
Wed.1 H 1028
New Frontiers in Splitting Algorithms for Optimization andMonotone Inclusions (Part I)
Organizer: Patrick L. Combettes
Chair: Patrick L. Combettes

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DER
Wed.1 H 2033
Emerging Trends in Derivative-Free Optimization (Part II)
Organizers: Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Chair: Nikolaus Hansen
Anne Auger, Inria (joint work with Dimo Brockhoff, Nikolaus
Hansen, Cheikh Touré)COMO-CMA-ES: a linearly convergent derivative freemulti-objective solver
We present a new multi-objective optimization solver,
COMO-CMA-ES that aim at converging towards p pointsof the Pareto set solution of a multi-objective problem. Denoting n the search space dimension, the solver approaches thep × n-dimensional problem of finding p solutions maximizing
the hypervolume by a dynamic subspace optimization. Eachsubspace optimization is performed with the CMA-ES solver.
We show empirically that COMO-CMA-ES converges linearlyon bi-convex-quadratic problems and that it has better performance than the MO-CMA-ES, NSGA-II and SMS-EMOA
algorithms.
Ludovic Salomon, Polytechnique Montréal (joint work withJean Bigeon, Sébastien Le Digabel)
MADMS: Mesh adaptive direct multisearch for blackbox
constrained multiobjective optimization

Wed.1 11:30–12:45

Derivative-free multiobjective optimization involves the presence of two or more conflicting objective functions, consideredas blackboxes for which no derivative information is available.
This talk describes a new extension of the Mesh Adaptive Direct Search (MADS) algorithm, called MADMS. This algorithm
keeps a list of non-dominated points which converges to thePareto front. As for the single-objective MADS algorithm, thismethod is built around an optional search step and a poll step.
Convergence results and promising computational experiments
will be described.
Ana Luisa Custodio, Universidade Nova de Lisboa (joint workwith Maria do Carmo Brás)
On the use of quadratic polynomial models in multiobjective directional direct search
Polynomial interpolation or regression models are an important tool in Derivative-free Optimization, acting as surrogatesof the real function. In this work we propose the use of thesemodels in a multiobjective framework, namely the one of Direct Multisearch. Previously evaluated points are used to build
quadratic polynomial models, which are minimized in an attempt of generating nondominated points of the true function,
defining a search step for the algorithm. We will detail the
proposed methodology and report compelling numerical results,
stating its competitiveness.

MUL
Wed.1 H 3002
Algorithmic Approaches to Multi-Objective Optimization
Organizers: Gabriele Eichfelder, Alexandra Schwartz
Chair: Christian GüntherYousuke Araya, Akita Prefectural University
Nonlinear scalarizations in set optimization problems
The investigation of nonlinear scalarizing functions for setsbegan around twenty years ago. Recently, many authors haveinvestigated nonlinear scalarization techniques for set optimization problems. In this presentation, first we look back into thehistory of nonlinear scalarization techniques for sets, and introduce three important papers published in the 2000s. Afterwards,
we present recent advances on this topic.
Christian Günther, Martin Luther University Halle-Wittenberg(joint work with Ali Farajzadeh, Bahareh Khazayel, Christiane
Tammer)Relationships between Clarke-Ye type penalization andvectorial penalization in multi-objective optimization
Penalization approaches in multi-objective optimization aim
to replace the original constrained optimization problem bysome related problems with an easier structured feasible set(e.g., unconstrained problems). One approach, namely the
Clarke-Ye type penalization method, consists of adding a penalization term with respect to the feasible set in each component
function of the objective function. In contrast, the vectorial
penalization approach is based on the idea to add a new penalization (component) function to the vector-valued objectivefunction. In this talk, we compare both approaches.
Stefan Banholzer, Universität Konstanz
Convex Multiobjective Optimization by the Reference
Point Method
This talk discusses the application of the reference point
method to convex multiobjective optimization problems with
arbitrarily many cost functions. The main challenge of thereference point method in the case of more than two costfunctions is to find reference points which guarantee a uniform
approximation of the whole Pareto front. In this talk we develop a theoretical description of the set of ’suitable’ reference
points, and show how this can be used to construct an iterative
numerical algorithm for solving the multiobjective optimization
problem.

Parallel Sessions Abstracts Wed.1 11:30–12:45

113

NON
Wed.1 H 0105
Geometry in Non-Convex Optimization (Part I)
Organizers: Nicolas Boumal, André Uschmajew, Bart Vandereycken
Chair: André Uschmajew

NON
Wed.1 H 1012
Nonlinear Optimization Methods and Their Global Ratesof Convergence
Organizer: Geovani Grapiglia
Chair: Geovani Grapiglia

Suvrit Sra, MIT
Riemannian optimization for some problems in probability
and statistics

Oliver Hinder, Stanford Univ. (joint work with Yair Carmon,John Duchi, Aaron Sidford)
The complexity of finding stationary points of nonconvex
functions

Nick Vannieuwenhoven, KU Leuven (joint work with Paul Breiding)Riemannian optimization for computing canonical polyadic
decompositions
The canonical tensor rank approximation problem consistsof approximating a real-valued tensor by one of low canonicalrank, which is a challenging non-linear, non-convex, constrained
optimization problem, where the constraint set forms a nonsmooth semi-algebraic set. We discuss Riemannian optimization methods for solving this problem for small-scale, dense
tensors. The proposed method displayed up to two orders ofmagnitude improvements in computational time for challenging
problems, as measured by the condition number of the tensor
rank decomposition.
Nicolas Boumal, Princeton University
Complexity of optimization on manifolds, and cubic regularization
Unconstrained optimization can be framed generally within
the framework of optimization on Riemannian manifolds. Overthe years, standard algorithms such as gradient descent, trust
regions and cubic regularization (to name a few) have been
extended to the Riemannian setting, with excellent numerical
success. I will show how, under appropriately chosen assumptions, their worst-case iteration complexity analyses carry overseamlessly from the Euclidean to the Riemannian case, then I
will discuss finer points related to these assumptions.

We investigate the complexity of finding stationary pointsof smooth (potentially) nonconvex functions. We provide both
new algorithms, adapting Nesterov’s accelerated methods to thenonconvex setting (achieving faster convergence than gradientdescent), and fundamental algorithm-independent lower bounds
on the complexity of finding stationary points. Our bounds
imply that gradient descent and cubic-regularized Newton’s
method are optimal within their natural function classes.
Philippe L. Toint, University of Namur (joint work with Stefania Bellavia, Coralia Cartis, Nick Gould, Gianmarco Gurioli,
Bendetta Morini)
Recent results in worst-case evaluation complexity forsmooth and non-smooth, exact and inexact, nonconvex
optimization
We present a review of results on the worst-case complexity of minimization algorithm for nonconvex problems using
potentially high-degree models. Global complexity bound arepresented that are valid for any model’s degree and any orderof optimality, thereby generalizing known results for first- andsecond-order methods. An adaptive regularization algorithm
using derivatives up to degree p will produce an -approximate
q-th order minimizer in at most O((p+1)/(p−q+1) ) evaluations.
We will also extend these results to the case of inexact objectivefunction and derivatives.
Ernesto G. Birgin, Universidade de São Paulo (joint work withJ. M. Martı́nez)
A Newton-like method with mixed factorizations and cubic
regularization and its usage in an Augmented Lagrangianframework
A Newton-like method for unconstrained minimization is
introduced that uses a single cheap factorization per iteration.
Convergence and complexity results, even in the case in which
the subproblems’ Hessians are far from being Hessians of theobjective function, are presented. When the Hessian is Lipschitzcontinuous, the proposed method enjoys O(ε−3/2 ) evaluation
complexity for first-order optimality and O(ε−3 ) for secondorder optimality. The usage of the introduced method forbound-constrained minimization and for nonlinear programmingis reported.

Wed.1 11:30–12:45

Non-convex optimization is typically intractable. This intractability can be surmounted if the problem possesses enoughstructure. This talk focuses on “geodesic convexity” as thisstructure. This hidden convexity not only yields proofs not
only of tractability but also suggests practical algorithmic
approaches. I will highlight several examples from machinelearning and statistics, where non-Euclidean geometry plays
a valuable role in obtaining efficient solutions to fundamental tasks (e.g., eigenvector computation, gaussian mixtures,
Wasserstein barycenters, etc.).

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NON
Wed.1 H 1029
Practical Aspects of Nonlinear Optimization
Organizer: Coralia Cartis
Chair: Tim Mitchell
Hiroshige Dan, Kansai University (joint work with Koei
Sakiyama)
Automatic Differentiation Software for Large-scale Nonlinear Optimization Problems
Automatic differentiation (AD) is essential for solving largescale nonlinear optimization problems. In this research, we
implement an AD software which is particularly designed forlarge-scale nonlinear optimization problems (NLPs). Large-scale
NLPs are usually expressed by indexed variables and functions
and our AD software can deal with such indexed elements effectively. Numerical experiments show the superiority of our AD
software for large-scale NLPs. Moreover, our AD software cancompute partial derivatives with arbitrary precision arithmetic.
Michael Feldmeier, The University of Edinburgh
structure detection in generic quadratic programming

Wed.1 11:30–12:45

Benders’ decomposition is a popular choice when solving
optimization problems with complicating variables. Implementations are often problem dependent, or at least require the user
to specify the master and sub-problems. In the past, attempts
have been made to detect automatically suitable structurein the constraint matrix of generic (mixed-integer) linear programmes. In this talk we discuss an extension of this approachto quadratic programming and will cover different approaches
to finding suitable structure in the constraint and Hessian
matrices. Initial numerical results will be given.
Tim Mitchell, Max Planck Institute for Dynamics of Complex
Technical Systems (joint work with Frank E. Curtis, Michael
Overton)Relative Minimization Profiles: A Different Perspective for
Benchmarking Optimization and Other Numerical Software
We present relative minimization profiles (RMPs) for benchmarking general optimization methods and other numericalalgorithms. Many other visualization tools, e.g., performance
profiles, consider success as binary: was a problem “solved” ornot. RMPs instead consider degrees of success, from obtaining
solutions to machine precision to just finding feasible ones.
RMPs concisely show how methods compare in terms of theamount of minimization or accuracy attained, finding feasible solutions, and speed of progress, i.e., how overall solution
quality changes for different computational budgets.

NON
Wed.1 H 2013
New Trends in Optimization Methods and Machine Learning (Part I)
Organizers: El houcine Bergou, Aritra Dutta
Chair: Aritra Dutta
Vyacheslav Kungurtsev, Czech Technical University in Prague(joint work with El houcine Bergou, Youssef Diouane, Clément
Royer)A Subsampling Line-Search Method with Second-OrderResults
Optimization of a large finite sum of nonconvex functions,such as appearing in the training of deep neural network architectures, suffers from two main challenges: 1) slow progressnear saddle points and 2) required a priori knowledge of problem constants to define a learning rate. In this talk we present
a stochastic line search method for finite sum optimization,in which first and second order information is sampled to define the step. We show thoretically that the method enjoys
a favorable complexity rate and numerical tests validate theperformance of the method.
El houcine Bergou, INRA-KAUST (joint work with Eduard Gorbunov, Peter Richtárik)
Stochastic Three Points Method for Unconstrained
Smooth Minimization
In this paper we consider the unconstrained minimization
problem of a smooth function in a setting where only function evaluations are possible. We design a novel randomized
derivative-free algorithm-the stochastic three points (STP)
method and analyze its iteration complexity. Given a current
iterate x, STP compares the objective function at three points:
x, x+αs and x-αs, where α > 0 is a stepsize parameter and s
is the random search direction. The best of these three points
is the next iterate. We analyze the method STP under several
stepsize selection schemes.
Xin Li, University of Central Florida
Remarks on a Greedy Randomized Kaczmarz Algorithm
After the randomized Kaczmarz algorithm of Thomas
Strohmer and Roman Vershynin was introduced, there havebeen many research papers on improving the algorithm further.
What exactly is the role of randomness in all the effort? We
will first give a brief survey on some recent works and pointout that in some cases, randomness is not needed at all. Thenwe will present a new modification of the algorithm that canhelp us to accelerate the convergence of the algorithm where
randomness is needed.

Parallel Sessions Abstracts Wed.1 11:30–12:45

Serge Gratton, INP-IRIT
Minimization of nonsmooth nonconvex functions using inexact evaluations and its worst-case complexity
An adaptive regularization algorithm using inexact function
and derivatives evaluations is proposed for the solution ofcomposite nonsmooth nonconvex optimization. It is shownthat this algorithm needs at most O(| log()|−2 ) evaluationsof the problem’s functions and their derivatives for findingan -approximate first-order stationary point. This complexity
bound is within a factor | log()| of the optimal bound known
for smooth and nonsmooth nonconvex minimization with exact
evaluations.
Selime Gurol, IRT Saint Exupéry / CERFACS (joint work withFrancois Gallard, Serge Gratton, Benoit Pauwels, Philippe L.
Toint)
Preconditioning of the L-BFGS-B algorithm for aerodynamic shape design
The classical L-BFGS-B algorithm, used to minimize an objective function depending on variables that must remain within
given bounds, is generalized to the case where the algorithm
accepts a general preconditioner. We will explain the main
ingredients of the preconditioned algorithm especially focusing on the efficient computation of the so-called generalized
Cauchy point. This algorithm is implemented in Python andis applied to the aerodynamic shape design problem, which
consists of solving several optimization problems, to reduce
the number of expensive objective function evaluations.
Elisa Riccietti, University of Toulouse (joint work with Henri
Calandra, Serge Gratton, Xavier Vasseur)
Multilevel optimization methods for the training of artificial neural networks
We investigate the use of multilevel methods (recursive
procedures that exploit the knowledge of a sequence of approximations to the original objective function, defined on spaces of
reduced dimension, to build alternative models to the standard
Taylor one, cheaper to minimize) to solve problems that do
not have an underlying geometrical structure. Specifically, we
focus on an important class of such problems, those arising in
the training of artificial neural networks. We propose a strategy
based on algebraic multigrid techniques to build the sequenceof coarse problems.

PDE
Wed.1 H 0106
Optimal Control of Nonsmooth Systems (Part I)
Organizers: Constantin Christof, Christian Clason
Chair: Christian Clason
Daniel Walter, RICAM
Accelerated generalized conditional gradient methods
We present a generalized version of the well-known FrankWolfe method for minimizing a composite functional j(u) =f (u) + g(u). Here, f is a smooth function and g is in general
nonsmooth but convex. The optimization space is given as thedual space of a separable, generally non-reflexive, Banach space .
We address theoretical properties of the method, such as worstcase convergence rates and mesh-independence results. For
specific instances of the problem we further show how additional
acceleration steps eventually lead to optimal convergence rates.
Lukas Hertlein, Technische Universität München (joint workwith Michael Ulbrich)
Optimal control of elliptic variational inequalities using
bundle methods in Hilbert space
Motivated by optimal control problems for elliptic variational
inequalities we develop an inexact bundle method for nonsmooth nonconvex optimization subject to general convex constraints. The proposed method requires only inexact evaluationsof the cost function and of an element of Clarke’s subdifferential.
A global convergence theory in a suitable infinite-dimensional
Hilbert space setting is presented. We discuss the application of
our framework to optimal control of the (stochastic) obstacle
problem and present numerical results.
Sebastian Engel, Technische Universität München (joint workwith Philip Trautmann, Boris Vexler)
Variational discretization for an optimal control problem
governed by the wave equation with time depending BV
controls
We will consider a control problem (P) for the wave equation
with a standard tracking-type cost functional and a semi-normin the space of functions with bounded variations (BV) intime. The considered controls in BV act as a forcing function
on the wave equation. This control problem is interesting forpractical applications because the semi-norm enhances optimal
controls which are piecewise constant. In this talk we focus
on a variationally discretized version of (P). Under specificassumptions we can present optimal convergence rates for thecontrols, states, and cost functionals.

Wed.1 11:30–12:45

NON
Wed.1 H 2053
Efficient Numerical Solution of Nonconvex Problems
Organizers: Serge Gratton, Elisa Riccietti
Chair: Elisa Riccietti

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PDE
Wed.1 H 0111
Optimal Control and Dynamical Systems (Part V)
Organizers: Cristopher Hermosilla, Michele Palladino
Chair: Michele Palladino
Franco Rampazzo, University of Padova
Infimum gap phenomena, set separation, and abnormality.
A general sufficient condition for avoiding infimum-gaps
when the domain of an optimal control problem is densely
embedded in a larger family of processes is here presented.
This condition generalizes some recently estalished cryteria for
convex or impulsive embedding, and is based on “abundantness”
a dynamics-related notion of density introduced by J. Warga.
Peter Wolenski, Louisiana State University
Optimality conditions for systems with piecewise constantdynamics
We will present results on the minimal time function wherethe velocity sets are locally the same.
Piernicola Bettiol, Université de Bretagne Occidentale (joint
work with Carlo Mariconda)
Some regularity results for minimizers in dynamic optimization

Wed.1 11:30–12:45

We consider the classical problem of the Calculus of Variations, and we formulate of a new Weierstrass-type condition for
local minimizers of the reference problem. It allows to derive
important properties for a broad class of problems involving
a nonautonomous, possibly extended-valued, Lagrangian. A
first consequence is the validity of a Du Bois-Reymond typenecessary condition, expressed in terms of convex subgradients. If the Lagrangian satisfies an additional growth condition(weaker than coercivity), this Weierstrass-type condition yields
the Lipschitz regularity of the minimizers.

PDE
Wed.1 H 0112
Fractional/Nonlocal PDEs: Applications, Control, and Beyond (Part II)
Organizers: Harbir Antil, Carlos Rautenberg, Mahamadii
Warma
Chair: Mahamadii Warma
Deepanshu Verma, George Mason University (joint work with
Harbir Antil, Mahamadii Warma)
External optimal control of fractional parabolic PDEs
We consider external optimal control with fractional parabolic
PDEs as constraints. The need stems from the fact that theclassical PDE models only allow placing the source either on theboundary or in the interior whereas the nonlocal behavior of thefractional operator allows placing the control in the exterior.
We introduce the notions of weak and very-weak solutions
to the parabolic Dirichlet problem and then approximate its
solutions by the parabolic Robin solutions. The numericalexamples confirm our theoretical findings and further illustrate
the potential benefits of nonlocal models.
Benjamin Manfred Horn, Technische Universität Darmstadt(joint work with Stefan Ulbrich)
A Bundle Trust-Region Method for Constrained Nonsmooth Problems applied to a Shape Optimization for Frictional Contact Problems
We present an optimization approach for nonsmooth, nonconvex and constraint optimization problems. The approachhas been developed in the context of shape optimization governed by a contact problem with Coulomb friction including
state gradient constraints. For the solution operator of the
weak formulation of the underlying variational inequality, we
prove the local Lipschitz continuity with respect to the geometry. The shape optimization problem is solved with a bundle
trust-region algorithm, whereas the handling of the nonlinear
constraints is motivated by a constraint linearization method.

Parallel Sessions Abstracts Wed.1 11:30–12:45

Fengpei Li,
Parametric Scenario Optimization under Limited Data: A
Distributionally Robust Optimization View
We show ways to leverage parametric information andMonte Carlo to obtain certified feasible solutions for chanceconstrained problems under limited data. Our approach makes
use of distributionally robust optimization (DRO) that translates the data size requirement in scenario optimization intoa Monte Carlo size requirement drawn from a generating distribution. We show that, while the optimal choice for such
distribution is in a sense, the baseline from a distance-based
DRO, it is not necessarily so in the parametric case, and leads
us to procedures that improve upon these basic choices.
Huajie Qian, Columbia University (joint work with Henry Lam)
Statistical calibration for robust optimization
Optimization formulations to handle data-driven decisionmaking under uncertain constraints, such as robust optimization, often encounter a statistical trade-off between feasibility
and optimality that potentially leads to over-conservative solutions. We exploit the intrinsic low dimensionality of the solution
sets possibly output from these formulations to enhance thistrade-off. For several common paradigms of data-driven optimization, we demonstrate the dimension-free performance of
our strategy in obtaining solutions that are, in a sense, bothfeasible and asymptotically optimal.
Yuanlu Bai, Columbia University (joint work with Henry Lam)
Extreme Event Estimation via Distributionally Robust Optimization
A bottleneck in analyzing extreme events is that, by theirown nature, tail data are often scarce. Conventional approaches
fit data using justified parametric distributions, but often encounter difficulties in confining both estimation biases andvariances. We discuss approaches using distributionally robust
optimization as a nonparametric alternative that, through adifferent conservativeness-variance tradeoff, can mitigate someof the statistical challenges in estimating tails. We explain thestatistical connections and comparisons of our framework withconventional extreme value theory.

ROB
Wed.1 H 3007
Recent Development in Applications of Distributionally
Robust Optimization
Organizer: Zhichao Zheng
Chair: Zhichao Zheng
Zhenzhen Yan, Nanyang Technological University
Data Driven Approach for Full Cut Promotion in Ecommerce
Full-cut promotion is a type of promotion where customers
spend a minimum threshold amount in a single order to enjoy
a fixed amount of discount for that order. In this paper, we
propose a sequential choice model to model consumer’s choiceof multiple items in a single transaction, which generalizes
the traditional choice model which assumes at most one item
chosen in one transaction. Base on the sequential choice model,
we further propose a data driven approach to optimize the cut
amount. We test the approach using both synthetic data anda real transaction data set from an e-commerce company
Guanglin Xu,
Data-Driven Distributionally Robust Appointment
Scheduling [canceled]
We study a single-server appointment scheduling problem
with a fixed sequence of appointments, for which we must determine the arrival time for each appointment. We specificallyexamine two stochastic models. In the first model, we assume
that all appointees show up at the scheduled arrival times yet
their service durations are random. In the second model, we
assume that appointees have random no-show behaviors andtheir service durations are random given that they show up at
the appointments. In both models, we assume that the probability distribution of the uncertain parameters is unknown but
can be partially observed via a set of historical observations. Inview of the distributional ambiguity, we propose a data-driven
distributionally robust optimization approach to determine anappointment schedule such that the worst-case (i.e., maximum)
expectation of the system total cost is minimized. A key feature of this approach is that the optimal value and the set of
optimal schedules thus obtained provably converge to those of
the “true” model, i.e., the stochastic appointment schedulingmodel with regard to the true probability distribution of thestochastic parameters. While the resulting optimization problems are computationally intractable, we reformulate them tocopositive programs, which are amenable to tractable semidefinite programming problems with high-quality approximations.
Furthermore, under some mild conditions, we reformulate the
distributioanlly robust appointment scheduling problems topolynomial-sized linear programs. Through an extensive numerical study, we demonstrate that our approach yields better
out-of-sample performance than two state-of-the-art methods.
Yini Gao, Singapore Management University (joint work with
Chung-Piaw Teo, Huan Zheng)
Disaster Relief Resource Preposition and Redeployment
Facing the Adversarial Nature
Large-scale natural disaster management is among the top
list of worldwide challenges. One of the difficulties faced isthat minimal historical information is present for future disaster prediction and risk assessment, making it challenging toconstruct proper ambiguity sets. In our paper, we model thenatural disaster as an adversary and adopt a game theoretical framework to characterize the interactions between theadversarial nature and a decision maker. We show that theinteraction can be fully captured by a conic program in generaland obtain a closed-form solution to a symmetric problem.

Wed.1 11:30–12:45

ROB
Wed.1 H 3006
Statistical Methods for Optimization Under Uncertainty
Organizer: Henry Lam
Chair: Henry Lam

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SPA
Wed.1 H 2038
Complexity of Sparse Optimization for Subspace Identification
Organizer: Julien Mairal
Chair: Julien Mairal
Guillaume Garrigos, Université Paris Diderot (joint work with
Jalal Fadili, Jerome Malick, Gabriel Peyré)
Model Consistency for Learning with low-complexity priors
We consider supervised learning problems where the prioron the coefficients of the estimator is an assumption of low
complexity. An important question in that setting is the oneof model consistency, which consists of recovering the correct
structure by minimizing a regularized empirical risk. Under someconditions, it is known that deterministic algorithms succeed
in recovering the correct structure, while a simple stochastic
gradient method fails to do so. In this talk, we provide thetheoretical underpinning of this behavior using the notion of
mirror-stratifiable regularizers.
Yifan Sun, UBC Vancouver (joint work with Halyun Jeong,
Julie Nutini, Mark Schmidt)
Are we there yet? Manifold identification of gradientrelated proximal methods

Wed.1 11:30–12:45

In machine learning, models that generalize better often generate outputs that lie on a low-dimensional manifold. Recently,
several works have shown finite-time manifold identification byproximal methods. In this work, we provide a unified view bygiving a simple condition under which any proximal method
using a constant step size can achieve finite-iteration manifold
detection. We provide iteration bounds for several key methods
such as (FISTA, DRS, ADMM, SVRG, SAGA, and RDA).
Julien Mairal, Inria (joint work with Andrei Kulunchakov)
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to
Noise
We propose a unified view of gradient-based algorithms forstochastic convex composite optimization based on estimate
sequences. This point of view covers stochastic gradient descent (SGD), the variance-reduction approaches SAGA, SVRG,
MISO, their proximal variants, and has several advantages:(i) we provide a generic proof of convergence for the aforementioned methods; (ii) we obtain new algorithms with thesame guarantees (iii) we derive generic strategies to makethese algorithms robust to stochastic noise (iv) we obtain new
accelerated algorithms.

STO
Wed.1 H 3012
Stochastic Optimization and Its Applications (Part I)
Organizer: Fengmin Xu
Chair: Fengmin Xu
Xiaojun Chen, Hong Kong Polytechnic University
Two-stage stochastic variational inequalities and noncooperative games
The two-stage stochastic variational inequality (SVI) provides a powerful modeling paradigm for many important applications in which uncertainties and equilibrium are present.
This talk reviews some new theory and algorithms for thetwo-stage SVI. Moreover, we formulate a convex two-stage
non-cooperative multi-agent game under uncertainty as a twostage SVI. Numerical results based on historical data in crude
oil market are presented to demonstrate the effectiveness of thetwo-stage SVI in describing the market share of oil producing
agents.
Jianming Shi, Tokyo University of Science
A Facet Pivoting Algorithm for Solving Linear Programming with Box Constraints
Linear programming (LP) is fundamentally important inmathematical optimization. In this talk, we review the following two methods: the LP-Newton method for LPs with box
constraints proposed by Fujishige et al. , LPS-Newton method
proposed by Kitahara et al. Inspired by the methods we develop
a new method that uses facets information of the constraints
for solving PLs with box constraints because an optimal solution can be found at an intersection between a zonotope and aline. We also report several results of preliminary experiments
showing the efficiency of the proposed algorithm.
Fengmin Xu, Xi’an Jiaotong University
Worst-case CVaR portfolio rebalancing with cardinalityand diversification constraints
In this paper, we attempt to provide a decision-practicalfoundation for optimal rebalancing strategies of an existing
financial portfolio. our main concern is the development ofa sparse and diversified portfolio rebalancing model in theworst-case CVaR framework, considering transaction costs anddouble cardinality constraints that trade off between the limits of investment scale and industry coverage requirements
simultaneously. We propose an efficient ADMM to conduct
out-of-sample performance analysis of our proposed strategy
on actual data from the Chinese stock market.

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119

Wed.2 14:15–15:30
APP

APP
Wed.2 H 3025
Mathematical Optimization in Signal Processing (Part II)
Organizers: Ya-Feng Liu, Zhi-Quan Luo
Chair: Ya-Feng Liu(FAU)Michael Stingl, Friedrich-Alexander-UniversitätErlangen-Nürnberg (joint work with Lukas Pflug)A new stochastic descent method for the efficient solutionof structural optimization problems with infinitely manyload cases

Zhi-Quan Luo, The Chinese University of Hong Kong, Shenzhen (joint work with Hamid Farmanbar, Navid Reyhanian)
A Decomposition Method for Optimal Capacity Reservation in Stochastic Networks

A stochastic gradient method for the solution of structural
optimization problems for which the objective function is given
as an integral of a desired property over a continuous parameter
set is presented. While the application of a quadrature rule leads
to undesired local minima, the new stochastic gradient method
does not rely on an approximation of the integral, requires
only one state solution per iteration and utilizes gradients fromprevious iterations in an optimal way. Convergence theory andnumerical examples including comparisons to the classical SG
and SAG method are shown.
Alemseged Gebrehiwot Weldeyesus, The University of Edinburgh (joint work with Jacek Gondzio)
Re-optimization and warm-starting in semidefinite programming
We focus on a semidefinite program arising in optimizationof globally stable truss structures. These problems are known
to be large-scale and very challenging to standard solution
techniques. We solve the problems using a primal-dual interior
point method, coupled with column generation procedure andwarm-start strategy. The implementation of the algorithm
extensively exploits the sparsity structure and low rank propertyof the involved matrices. We report on a solution of very large
problems which otherwise would be prohibitively expensive forexisting semidefinite programming solvers.
Jonas Holley, Robert Bosch GmbH (joint work with Michael
Hintermüller)
Topological Derivatives for the Optimization of an Electrical MachineThis talk is concerned with an adjoint-based topology optimization method for designing an electrical machine. In order
to meet the requirements of real industrial applications, different objectives regarding energy efficiency, as well as mechanicalrobustness need to be addressed. The resulting optimization
problem is challenging due to pointwise state constraints, which
are tackled by a penalization approach. A line search type gradient descent scheme is presented for the numerical solutionof the optimization problem, where the topological derivative
serves as a descent direction.

We consider the problem of reserving link capacity/resourcesin a network in such a way that a given set of scenarios (possibly
stochastic) can be supported. In the optimal capacity/resource
reservation problem, we choose the reserved link capacities
to minimize the reservation cost. This problem reduces toa large nonlinear program, with the number of variables andconstraints on the order of the number of links times the numberof scenarios. We develop an efficient decomposition algorithm
for the problem with a provable convergence guarantee.
Ya-Feng Liu, Chinese Academy of Sciences
Semidefinite Relaxations for MIMO Detection: Tightness,
Tighterness, and Beyond
Multiple-input multi-output (MIMO) detection is a fundamental problem in modern digital communications. Semidefinite relaxation (SDR) based algorithms are a popular class ofapproaches to solving the problem. In this talk, we shall firstdevelop two new SDRs for MIMO detection and show theirtightness under a sufficient condition. This result answers an
open question posed by So in 2010. Then, we shall talk about
the tighterness relationship between some existing SDRs forthe problem in the literature. Finally, we shall briefly talk about
the global algorithm based on the newly derived SDR.
Wed.2 14:15–15:30

Wed.2 H 3004
Structural Design Optimization (Part II)
Organizer: Tamás Terlaky
Chair: Tamás Terlaky

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BIG
Wed.2 H 0104
Recent Advancements in Optimization Methods for Machine Learning (Part III)
Organizers: Albert Berahas, Martin Takáč
Chair: Martin Takáč

BIG
Wed.2 H 0110
Recent Advances in Convex and Non-Convex Optimization
and Their Applications in Machine Learning (Part I)
Organizers: Qihang Lin, Tianbao Yang
Chair: Qihang Lin

Peter Richtárik, King Abdullah University of science and technology (KAUST) (joint work with Filip Hanzely, Konstantin
Mishchenko)
SEGA: Variance Reduction via Gradient Sketching

Yiming Ying, SUNY Albany
Stochastic Optimization for AUC Maximization in Machine Learning

We propose a randomized first order optimization method–SEGA (SkEtched GrAdient method)– which progressively
throughout its iterations builds a variance-reduced estimateof the gradient from random linear measurements (sketches)of the gradient obtained from an oracle. In each iteration,
SEGA updates the current estimate of the gradient through asketch-and-project operation using the information provided
by the latest sketch, and this is subsequently used to computean unbiased estimate of the true gradient through a random
relaxation procedure.
Aaron Defazio, Facebook
Optimization, Initialization and Preconditioning of Modern ReLU Networks [canceled]Wed.2 14:15–15:30

In this talk I will discuss a number of issues encounteredwhen optimizing modern ReLU based deep neural networks
such as the ResNet-50 model, and suggest some solutions.
Topics include:- Improving the conditioning of the Hessian using a careful
initialization scheme.
- Guiding the design of networks by following a scaling rulethat improves the conditioning of the Hessian, resulting in lessguess work and faster optimization.
- Avoiding instability at the beginning of learning by usingbalanced ReLUs.
- Variance reduction for deep learning
Aurelien Lucchi, ETH Zürich
Continuous-time Models for Accelerated Stochastic Optimization Algorithms
We focus our discussion on accelerated methods for nonconvex and stochastic optimization problems. The choice of
how to memorize information to build the momentum hasa significant effect on the convergence properties of an accelerated method. We first derive a general continuous-timemodel that can incorporate arbitrary types of memory. We thendemonstrate how to discretize such process while matching
the same rate of convergence as the continuous-time model.
We will also discuss modern optimization techniques used inmachine learning such as Adam and RMSprop.

In this talk, I will present our recent work on developing
novel SGD-type algorithms for AUC maximization. Our new
algorithms can allow general loss functions and penalty terms
which are achieved through the innovative interactions between
machine learning and applied mathematics. Compared with theprevious literature which requires high storage and per-iteration
costs, our algorithms have both space and per-iteration costsof one datum while achieving optimal convergence rates.
Niao He, University of Illinois at Urbana-Champaign
Sample Complexity of Conditional Stochastic Optimization
We study a class of compositional stochastic optimization
involving conditional expectations, which finds a wide spectrum of applications, particularly in reinforcement learning androbust learning. We establish the sample complexities of amodified Sample Average Approximation (SAA) and a biased
Stochastic Approximation (SA) for such problems, under various structural assumptions such as smoothness and quadratic
growth conditions. Our analysis shows that both modified SAA
and biased SA achieve the same complexity bound, and ourexperiments further demonstrate that these bounds are tight.
Javier Pena, Carnegie Mellon University
A unified framework for Bregman proximal methods
We propose a unified framework for analyzing the convergence of Bregman proximal first-order algorithms for convex
minimization. Our framework hinges on properties of the convex conjugate and gives novel proofs of the convergence rates of
the Bregman proximal subgradient, Bregman proximal gradient,and a new accelerated Bregman proximal gradient algorithm
under fairly general and mild assumptions. Our accelerated
Bregman proximal gradient algorithm attains the best-known
accelerated rate of convergence when suitable relative smoothness and triangle scaling assumptions hold.

Parallel Sessions Abstracts Wed.2 14:15–15:30

Abhishek Singh, Indian Institute of Technology (BHU) Varanasi(joint work with Debdas Ghosh)Generalized Nash Equilibrium problems with Inexact line
search Newton method
In this paper, we study an inexact line search Newton method
to solve a generalized Nash equilibrium problem (GNEP). The
GNEP is an extension of classical Nash equilibrium problem,
where feasible set depends on the strategies of the other players.
In this paper, we have taken a particular class of GNEP in
which the objective function and the constraint mappings areconvex. The proposed method converges globally and also has
a faster rate of convergence as compared to previous methods.

Jo Andrea Brüggemann, Weierstrass Institute for Applied Analysis and Stochastics (WIAS) (joint work with Michael Hintermüller, Carlos Rautenberg)
Solution methods for a class of obstacle-type quasi variational inequalities with volume constraints
We consider compliant obstacle problems, where two elastic membranes enclose a constant volume. The existence ofsolutions to this quasi-variational inequality (QVI)is established building on fixed-point arguments and partly on Moscoconvergence. Founded on the analytical findings, the solutionof the QVI is approached by solving a sequence of variational
inequalities (VIs). Each of these VIs is tackled in function
space via a path-following semismooth Newton method. The
numerical performance of the algorithm is enhanced by using
adaptive finite elements based on a posteriori error estimation.
Ruchi Sandilya, Weierstrass Institute for Applied Analysis andStochastics (WIAS) (joint work with Sarvesh Kumar, Ricardo
Ruiz-Baier)Error bounds for discontinuous finite volume discretisations of Brinkman optimal control problems
We introduce a discontinuous finite volume method for theapproximation of distributed optimal control problems governedby the Brinkman equations, where a force field is sought suchthat it produces a desired velocity profile. The discretisationof state and co-state variables follows a lowest-order scheme,
whereas three different approaches are used for the control
representation: a variational discretisation and approximation
through piecewise constant or piecewise linear elements. We
employ the optimise-then-discretise approach, resulting in a
non-symmetric discrete formulation. A priori error estimates forvelocity, pressure, and control in natural norms are derived, anda set of numerical examples is presented to illustrate the performance of the method and to confirm the predicted accuracyof the generated approximations under various scenarios.

CON
Wed.2 H 0107
Advances in Convex Optimization
Organizer: Negar Soheili
Chair: Negar Soheili
Robert Freund, Massachusetts Institute of Technology (joint
work with Jourdain Lamperski, Michael Todd)
An oblivious ellipsoid algorithm for solving a system of(in)feasible linear inequalities
Consider the ellipsoid method applied to compute a point
in P where P is represented as the feasible solutions of asystem of linear inequalities. When P is empty, there exists acertificate of infeasibility (via a theorem of the alternative), yet
the standard ellipsoid method does not automatically furnish
such a certificate. In this talk we present a modified version of
the ellipsoid method that computes a point in P when P isnonempty, and computes a certificate of infeasibility when P
is empty, and whose complexity depends on max(m, n) andon a natural condition number of the problem.
Michael Friedlander, University of British Columbia
Polar duality and atomic alignment
The aim of structured optimization is to assemble a solution,using a given set of atoms, to fit a model to data. Polarity,
which generalizes the notion of orthogonality from linear sets
to general convex sets, plays a special role in a simple andgeometric form of convex duality. The atoms and their implicit“duals” share a special relationship, and their participation in
the solution assembly depends on a notion of alignment. Thisgeometric perspective leads to practical algorithms for largescale problems.
Negar Soheili, University of Illinois at Chicago (joint work with
Qihang Lin, Selva Nadarajah, Tianbao Yang)
A Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints
Stochastic optimization problems with expectation constraints (SOEC) arise in several applications. Only recently
has an efficient stochastic subgradient method been developed
for solving a SOEC defined by an objective and a single inequality constraint, where both are defined by expectations of
stochastic convex functions. We develop a level-set method
to compute near-optimal solutions to a generalization of thisSOEC containing multiple inequality constraints, each involving
an expectation of a stochastic convex function. We establish
convergence guarantees for this method.

Wed.2 14:15–15:30

VIS
Wed.2 H 3013
Quasi-Variational Inequalities and Generalized Nash Equilibrium Problems (Part II)
Organizers: Amal Alphonse, Carlos Rautenberg
Chair: Amal Alphonse

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CON
Wed.2 H 2032
Polynomial Optimization (Part II)
Organizer: Grigoriy Blekherman
Chair: Grigoriy Blekherman
Etienne de Klerk, Tilburg University (joint work with Monique
Laurent)
Convergence analysis of a Lasserre hierarchy of upperbounds for polynomial minimization on the sphereWe study the convergence rate of a hierarchy of upper bounds
for polynomial minimization problems, proposed by Lasserre(2011), for the special case when the feasible set is the unit
(hyper)sphere. The upper bound at level r of the hierarchy isdefined as the minimal expected value of the polynomial overall probability distributions on the sphere, when the probability
density function is a sum-of-squares polynomial of degree at
most 2r with respect to the surface measure. We show thatthe exact rate of convergence is of order 1/r2 . (Joint workwith Monique Laurent.)
Cordian Riener, Uit (joint work with J. Rolfes, F. Vallentin,
David de Laat)
Lower bounds for the covering problem of compact metricspacesLet (X, d) be a compact metric space. For a given positive
number r we are interested in the minimum number of ballsof radius r needed to cover X. We provide a sequence of
infinite-dimensional conic programs each of which yields a lower
bound for this number and show that the resulting sequence oflower bounds converges in finitely many steps to the minimal
number of balls needed. These programs satisfy strong duality
and we can derive a finite dimensional semidefinite program
to approximate the optimal solution to arbitrary precision.
Wed.2 14:15–15:30

Victor Magron, LAAS-CNRS (joint work with Jean-Bernard
Lasserre, Mohab Safey El Din)
Two-player games between polynomial optimizers,
semidefinite solvers and certifiers
We interpret some wrong results, due to numerical inaccuracies, observed when solving semidefinite programming (SDP)
relaxations for polynomial optimization. The SDP solver performs robust optimization and the procedure can be viewed asa max-min problem with two players. Next, we consider the
problem of finding exact sums of squares (SOS) decompositions
with SDP solvers. We provide a perturbation-compensation
algorithm computing exact decompositions for elements in theinterior of the SOS cone. We prove that this algorithm has apolynomial boolean running time w.r.t. the input degree.

CNV
Wed.2 H 1028
New Frontiers in Splitting Algorithms for Optimization andMonotone Inclusions (Part II)
Organizer: Patrick L. Combettes
Chair: Patrick L. Combettes
Calvin Wylie, Cornell University (joint work with Adrian S.
Lewis)
Partial smoothness, partial information, and fast local algorithms
The idea of partial smoothness unifies active-set andconstant-rank properties in optimization and variational inequalities. We sketch two local algorithmic schemes in partly
smooth settings. One promising heuristic solves finite minimax
subproblems using only partial knowledge of the component
functions. The second, more conceptual, approach relies on
splitting schemes for variational inequalities.
Isao Yamada, Tokyo Institute of Technology (joint work with
Masao Yamagishi)
Global optimization of sum of convex and nonconvex functions with proximal splitting techniques
In sparsity aware signal processing and machine learning, ideal
optimization models are often formulated as a minimizationof a cost function defined as the sum of convex and possibly
nonconvex terms of which the globally optimal solution is
hard to compute in general. We present a unified translationof a certain class of such convex optimization problems intomonotone inclusion problems and propose iterative algorithms
of guaranteed convergence to a globally optimal solution.
Christian L. Müller, Flatiron Institute (joint work with Patrick
L. Combettes)
Perspective maximum likelihood-type estimation via proximal decomposition
We introduce an optimization model for general maximum
likelihood-type estimation (M-estimation). The model leverages
the observation that convex M-estimators with concomitantscale as well as various regularizers are instances of perspectivefunctions. Such functions are amenable to proximal analysis
and solution methods via splitting techniques. Using a geometrical approach based on duality, we derive novel proximity
operators for several perspective functions. Numerical examples
are provided.

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123

CNV
Wed.2 H 1058
Algorithms for Large-Scale Convex and Nonsmooth Optimization (Part I)
Organizer: Daniel Robinson
Chair: Daniel Robinson

DER
Wed.2 H 2033
Model-Based Derivative-Free Optimization Methods
Organizers: Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Chair: Margherita Porcelli

Guilherme Franca, Johns Hopkins University (joint work with
Daniel Robinson, Rene Vidal)
Conformal Symplectic Optimization

Raghu Pasupathy, Purdue University
Stochastic Trust Region Methods ASTRO and ASTRODF: Convergence, Complexity, and Numerical Performance

Zhihui Zhu, Johns Hopkins University (joint work with Daniel
Robinson, Manolis Tsakiris, Rene Vidal)
A Linearly Convergent Method for Nonsmooth Nonconvex
Optimization on the Sphere with Applications to Robust
PCA and Orthogonal Dictionary Learning
We consider the minimization of a general nonsmooth function over the unit sphere. We show that if the objective satisfies
a certain Riemannian regularity condition relative to a set B,
a Riemannian subgradient method with an appropriate initialization and geometrically diminishing step size converges at alinear rate to the set B. We also show that this regularity condition holds for orthogonal dictionary learning (ODL) and Dual
Principal Component Pursuit (DPCP) under suitable choicesof B. This leads to important improvements upon the known
convergence results for both ODL and DPCP.
Sergey Guminov, IITP, Russian Academy of Sciences (joint
work with Vladislav Elsukov, Alexander Gasnikov)
Accelerated Alternating Minimization
Alternating minimization (AM) optimization algorithms have
known for a long time. These algorithms assume that the decision variable is divided into several blocks and minimizationin each block can be done explicitly. AM algorithms have anumber of applications in machine learning problems. In thispaper, we are mostly motivated by optimal transport applications, which have become important for the machine learning
community. The ubiquitous Sinkhorn algorithm can be seen
as an alternating minimization algorithm for the dual to theentropy-regularized optimal transport problem. Sublinear 1/kconvergence rate was proved for AM algorithm. Despite thesame convergence rate as for the gradient descent method,AM-algorithms typically converge faster in practice as they
are free of the choice of the step-size and are adaptive to the
local smoothness of the problem. At the same time, there areaccelerated gradient methods which use a momentum term tohave a faster convergence rate of 1/k2 . We introduce an accelerated alternating minimization method with 1/k2 convergence
rate, where k is the iteration counter. We also show that the
proposed method is primal-dual, meaning that if we apply it toa dual problem, we can reconstruct the solution of the primal
problem with the same convergence rate. We apply our method
to the entropy regularized optimal transport problem and show
experimentally, that it outperforms Sinkhorn’s algorithm.

ASTRO is an adaptive sampling trust region algorithm designed to solve (unconstrained) smooth stochastic optimization
problems with a first-order oracle constructed using a Monte
Carlo simulation or a large dataset of “scenarios.” Like its
derivative-free counterpart ASTRO-DF, ASTRO adaptively
constructs a stochastic local model, which is then optimized
and updated iteratively. Crucially and unlike ASTRO-DF, however, the extent of sampling in ASTRO is directly proportional
to the inverse squared norm of the sampled gradient at the incumbent point, leading to clearly observable gains in numericalperformance. The sequence of (true) gradient norms measured
at ASTRO’s iterates converges almost surely to zero. We alsocharacterize a work complexity result that expresses ASTRO’sconvergence rate in terms of the total number of oracle calls(as opposed to the total number of iterations). This appears
to be one of the few results of its kind and confirms the strong
numerical performance observed in practice.
Lindon Roberts, University of Oxford (joint work with Coralia
Cartis, Jan Fiala, Benjamin Marteau)
Improving the scalability of derivative-free optimization for
nonlinear least-squares problems
In existing techniques for model-based derivative-free optimization, the computational cost of constructing local models
and Lagrange polynomials can be high. As a result, these algorithms are not as suitable for large-scale problems as derivativebased methods. In this talk, I will introduce a derivative-freemethod based on exploration of random subspaces, suitable fornonlinear least-squares problems. This method has a substantially reduced computational cost (in terms of linear algebra),
while still making progress using few objective evaluations.
Liyuan Cao, Lehigh University (joint work with Albert Berahas,
Katya Scheinberg)
Derivative Approximation of some Model-based Derivative
Free Methods
To optimize a black-box function, gradient-based methods
can be used in conjunction with finite difference methods.
Alternatively, optimization can be done with polynomial interpolation models that approximate the black-box function. A
class of random gradient-free algorithms based on Gaussiansmoothing of the objective function was analyzed by Yurii Nesterov in 2011. Similar random algorithms were implemented
and discussed in the machine learning community in more
recent years. We compare these methods on their accuracy ofgradient approximation and the overall performance.

Wed.2 14:15–15:30

A promising direction to study accelerated methods has
been emerging through connections with continuous dynamicalsystems. However, it is unclear whether main properties of the
dynamical system is preserved by such algorithms. We will showthat the classical momentum method is a conformal symplectic
integrator, i.e. it preserves the symplectic structure of the
dynamical system. On the other hand, Nesterov’s accelerated
gradient is not a structure preserving discretization. We willcomment on generalizations of these methods and introduce a
symplectic alternative to Nesterov’s method.

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GLO
Wed.2 H 3008
Global Optimization - Contributed Session I
Chair: Syuuji Yamada
Kuan Lu, Tokyo Institute of Technology (joint work with Shinji
Mizuno, Jianming Shi)
Solving Optimization over the Efficient Set of a Multiobjective Linear Programming Problem as a Mixed Integer
Problem

NON
Wed.2 H 0105
Geometry in Non-Convex Optimization (Part II)
Organizers: Nicolas Boumal, André Uschmajew, Bart Vandereycken
Chair: Bart Vandereycken
Marco Sutti, University of Geneva
Multilevel optimization on low-rank manifolds for optimal
control problems

Wed.2 14:15–15:30

This research concerns an optimization problem over theefficient set of a multiobjective linear programming problem.
We show that the problem is equivalent to a mixed integer
programming (MIP) problem, and we propose to compute
an optimal solution by solving the MIP problem. Comparedwith the previous mixed integer programming approach, theproposed approach relaxes a condition for the multiobjective
linear programming problem and reduces the size of the MIP
problem. The MIP problem can be efficiently solved by current
MIP solvers when the objective function is convex or linear.

Large-scale finite-dimensional optimization problems sometimes admit solutions that can be well approximated by lowrank matrices and tensors. In this talk, we will exploit thislow-rank approximation property by solving the optimization
problem directly over the set of low rank matrices and tensors.
In particular, we introduce a new multilevel algorithm, where
the optimization variable is constrained to the Riemannian
manifold of fixed-rank matrices, allowing to keep the ranks(and thus the computational complexity) fixed throughout theiterations. This is a joint work with Bart Vandereycken.

Francesco Romito, Sapienza University of Rome (joint workwith Giampaolo Liuzzi, Stefano Lucidi, Veronica Piccialli)
Exploiting recursive non-linear transformations of the variables space in global optimization problems

Max Pfeffer, Max Planck Institute for Mathematics in the Sciences
Optimization methods for computing low rank eigenspaces

Solving a global optimization problem is a hard task. We
define recursive non-linear equations that are able to transformthe variables space and allow us to tackle the problem inan advantageous way. The idea is to exploit the information
obtained during a multi-start approach in order to define asequence of transformations in the variables space. The aim isthat of expanding the basin of attraction of global minimizers
while shrinking those of already found local minima.

We consider the task of approximating the eigenspace belonging to the lowest eigenvalues of a self adjoint operator ona space of matrices, with the condition that it is spanned bylow rank matrices that share a common row space of small
dimension. Such a problem arises for example in the DMRG
algorithm in quantum chemistry. We propose a Riemannian
optimization method based on trace minimization that takes
orthogonality and low rank constraints into account simultaneously, and shows better numerical results in certain scenarios
compared to other current methods.

Syuuji Yamada, Niigata University
A global optimization algorithm by listing KKT points fora quadratic reverse convex programming problem
In this paper, we propose a procedure for listing KKT pointsof a quadratic reverse convex programming problem (QRC)
whose feasible set is expressed as the area excluded the interior of a convex set from another convex set. Since it isdifficult to solve (QRC), we transform (QRC) into a parametricquadratic programming problem (QP). In order to solve (QP)
for each parameter, we introduce an algorithm for listing KKTpoints. Moreover, we propose an global optimization algorithmfor (QRC) by incorporating our KKT listing algorithm into aparametric optimization method.

Yuqian Zhang, Cornell University
Nonconvex geometry and algorithms for blind deconvolution
Blind deconvolution is an ill-posed problem aiming to recover a convolution kernel and an activation signal from theirconvolution. We focus on the short and sparse variant, where
the convolution kernel is short and the activation signal issparsely and randomly supported. This variant models convolutional signals in several important application scenarios.
The observation is invariant up to some mutual scaling andshift of the convolutional pairs. This scaled-shift symmetry is
intrinsic to the convolution operator and imposes challenges
for reliable algorithm design. We normalize the convolution
kernel to have unit Frobenius norm and then cast the blind
deconvolution problem as a nonconvex optimization problem
over the sphere. We demonstrate that (i) under conditions,
every local optimum is close to some shift truncation of theground truth, and (ii) for a generic filter of length k, when thesparsity of activation signal satisfies θ < k3/4 and number ofmeasurements m > poly(k), provable recovery of the groundtruth signals can be obtained.

Parallel Sessions Abstracts Wed.2 14:15–15:30

Jun-ya Gotoh, Chuo University (joint work with Takumi
Fukuda)Sparse recovery with continuous exact k-sparse penalties
This paper presents efficient algorithms for k-sparse recovery problem on the basis of the Continuous Exact k-Sparse
Penalties (CXPs). Each CXP is defined by a non-convex but
continuous function and the equality forcing the function value
to be zero is known to be equivalent to the k-sparse constraint
defined with the so-called `0 -norm. Algorithms based on theproximal maps of the `1 -norm or `2 -norm based CXPs will be
examined for the sparse recovery problems with/without noise.
Shummin Nakayama, Chuo University
Inexact proximal memoryless quasi-Newton methods
based on Broyden family for minimizing composite functions
In this talk, we consider proximal Newton-type methods forthe minimization of a composite function that is the sum of asmooth function and a nonsmooth function. Proximal Newtontype methods make use of weighted proximal mappings whose
calculations are much expensive. Accordingly, some researchers
have proposed inexact Newton-type proximal methods, whichcalculate the proximal mapping inexactly. To calculate proximal
mappings more easily, we propose an inexact proximal method
based on memoryless quasi-Newton methods with the Broyden
family. We analyze global and local convergence properties.
Takayuki Okuno, RIKEN AIP (joint work with Akihiro Kawana,
Akiko Takeda)
Hyperparameter optimization for lq regularizer via bilevel
optimization
We propose a bilevel optimization strategy for selecting thebest hyperparameter value for the nonsmooth lp regularizer
with 0 < p ≤ 1. The concerned bilevel optimization problem
has a nonsmooth, possibly nonconvex, `p -regularized problem
as the lower-level problem. For solving this problem, we propose a smoothing-type algorithm and show that a sequence
generated by the proposed method converges to a point satisfying certain optimality conditions. We also conducted somenumerical experiments to exhibit the efficiency by comparing
it with Bayesian optimization.

NON
Wed.2 H 1029
Advances in Nonlinear Optimization Algorithms (Part I)
Chair: Mehiddin Al-Baali
Johannes Brust, Argonne National Laboratory (joint work with
Roummel Marcia, Cosmin Petra)
Large-Scale Quasi-Newton Trust-Region Methods with
Low-Dimensional Equality Constraints
For large-scale optimization problems, limited-memory quasiNewton methods are efficient means to estimate Hessian matrices. We present two L-BFGS trust-region methods for large
optimization problems with a low number of linear equality
constraints. Both methods exploit a compact representationof the (1, 1) block of the inverse KKT matrix. One of the proposed methods uses an implicit eigendecomposition in order tocompute search directions by an analytic formula, and the otheruses a 1D root solver. We compare the methods to alternative
quasi-Newton algorithms on a set of CUTEst problems.
Hardik Tankaria, Kyoto University (joint work with Nobuo Yamashita)
Non-monotone regularized limited memory BFGS methodwith line search for unconstrained optimization
Recently, Sugimoto and Yamashita proposed the regularized
limited memory BFGS (RL-BFGS) with non-monotone technique for unconstrained optimization and reported that it iscompetitive to the standard L-BFGS. However, RL-BFGS does
not use line search; hence it cannot take longer step. In orderto take longer step, we propose to use line search with Wolfe
condition when iteration of RL-BFGS is successful and thesearch direction is still descent direction. The numerical resultsshows that RL-BFGS with the proposed technique is more
efficient and stable than the standard L-BFGS and RL-BFGS
method.
Mehiddin Al-Baali, Sultan Qaboos University
Conjugate Gradient Methods with Quasi-Newton Features
for Unconstrained Optimization
The recent class of symmetric conjugate gradient methods
for large-scale unconstrained optimization will be considered.
The quasi-Newton like condition will be introduced to thisclass of methods in a sense to be defined. It will be shown thatthe proposed methods converge globally and has some usefulfeatures. Numerical results will be described to illustrate thebehavior of some new techniques for improving the performanceof several conjugate gradient methods substantially.

Wed.2 14:15–15:30

NON
Wed.2 H 1012
Developments in Structured Nonconvex Optimization(Part I)
Organizers: Jun-ya Gotoh, Yuji Nakatsukasa, Akiko Takeda
Chair: Yuji Nakatsukasa

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NON
Wed.2 H 2013
New Trends in Optimization Methods and Machine Learning (Part II)
Organizers: El houcine Bergou, Aritra Dutta
Chair: El houcine Bergou

NON
Wed.2 H 2053
Recent Trends in Large Scale Nonlinear Optimization(Part I)
Organizers: Michal Kočvara, Panos Parpas
Chair: Michal Kočvara

Aritra Dutta, King Abdullah University of Science and Technology (KAUST) (joint work with Boqing Gong, Xin Li, Mubarak
Shah)
On a Weighted Singular Value Thresholding Algorithm

Derek Driggs, University of Cambridge (joint work with
Matthias Ehrhardt, Carola-Bibiane Schönlieb)
Stochastic Optimisation for Medical Imaging

Sparse and low-rank approximation problems have many
crucial applications in computer vision and recommendation
systems. In this paper, we formulate and study a weighted singular value thresholding (WSVT) problem. Unlike many papers
in the literature on generalized SVT methods such as generalized SVT and weighted nuclear norm minimization, we do not
modify the nuclear norm in SVT. Instead, we use a weighted
Frobenius norm combined with the nuclear norm. We present
an algorithm to numerically solve our WSVT problem andestablish the convergence of the algorithm. As a proof of concept, we extensively apply WSVT to a variety of applications,
including removal of shadows, inlier detection, and backgroundestimation and demonstrate that the weighted Frobenius normin our WSVT can work as efficiently as the L1 norm usedin the Robust PCA algorithms. Moreover, we match or outperform state-of-the-art principal component pursuit (PCP)
algorithms in almost all quantitative and qualitative measuresand in fractions of their execution time. This indicates thatwith a properly inferred weight matrix, our computationallyinexpensive WSVT algorithm can serve as a natural alternative
to the PCP problem.

Wed.2 14:15–15:30

Michel Barlaud, University of Nice Sophia Antipolis (joint workwith Jean-Baptiste Caillau, Antonin Chambolle)
Robust supervised classification and feature selectionusinga primal-dual method
This paper deals with supervised classification and feature
selection in high dimensional space. A classical approach isto project data on a low dimensional space and classify byminimizing an appropriate quadratic cost. It is well known thatusing a quadratic cost is not robust to outliers. We cope withthis problem by using an `1 norm both for the constraint andfor the loss function. In this paper, we provide a novel tailored
constrained primal-dual method with tconvergence proofs tocompute jointly selected features and classifiers.
Jakub Mareček, IBM ResearchTime-varying Non-convex Optimisation: Three Case Studies
We consider three prominent examples of non-convex optimisation in power systems (e.g., alternating current optimal
power flow), low-rank modelling (e.g., background subtraction
in computer vision), and learning linear dynamical systems(e.g., time-series forecasting). There has been a considerable
recent progress in tackling such problems, via hierarchies of
convexifications or otherwise, at a price of non-trivial run-time.
We study the three examples in a setting, where a sequenceof time-varying instances is available. We showcase tracking
results and regret bounds.

Stochastic gradient methods have cemented their role as
workhorses for solving problems in machine learning, but it
is still unclear if they can have the same success solving inverse problems in imaging. In this talk, we explore the uniquechallenges that stochastic gradient methods face when solving
imaging problems. We also propose a family of accelerated
stochastic gradient methods with fast convergence rates. Our
numerical experiments demonstrate that these algorithms cansolve imaging and machine learning problems faster than existing methods.
Zhen Shao, University of Oxford (joint work with Coralia Cartis,
Jan Fiala)
Sketching for sparse linear least squares
We discuss sketching techniques for sparse Linear Least
Squares (LLS). We give theoretical bounds for the accuracy ofthe sketched solution/residual when hashing matrices are used
for sketching, quantifying carefully the trade-off between thecoherence of the original matrix and the sparsity of the hashing
matrix. We use these to quantify the success of our algorithm
that employs a sparse factorisation of the sketched matrix as apreconditioner for the original LLS before applying LSQR. We
extensively compare our algorithm to state-of-the-art direct
and iterative solvers for large sparse LLS.
Panos Parpas, Imperial College London
Distributed optimization with probabilistically consistentmodelsWe introduce a framework for developing and analyzing
distributed optimization algorithms that are guaranteed toconverge under very general conditions. The conditions we
consider include the use of reduced order and approximate
models, and we only assume that the approximate models are
consistent with a certain probability. To motivate the theory,
we report preliminary results from using a particular instanceof the proposed framework for solving large scale non-convex
optimization models.

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PDE

127

PDE

Wed.2 H 0106
Optimal Control of Nonsmooth Systems (Part II)
Organizers: Constantin Christof, Christian Clason
Chair: Constantin Christof

Wed.2 H 0111
Optimal Control and Dynamical Systems (Part VI)
Organizers: Cristopher Hermosilla, Michele Palladino
Chair: Michele Palladino

Dorothee Knees, University of Kassel (joint work with
Stephanie Thomas)
Optimal control of rate-independent systems

Paloma Schäfer Aguilar, TU Darmstadt (joint work with Stefan
Ulbrich)Numerical approximation of optimal control problems forconservation laws

Vu Huu Nhu, University of Duisburg-Essen (joint work with
Christian Clason, Arnd Rösch)
Optimal control of a non-smooth quasilinear elliptic equation
This talk is concerned with an optimal control problem
governed by a non-smooth quasilinear elliptic equation with anonlinear coefficient in the principal part that is locally Lipschitz
continuous and directionally but not Gâteaux differentiable.
Based on a suitable regularization scheme, we derive C- andstrong stationarity conditions. Under the additional assumptionthat the nonlinearity is a P C 1 function with countably many
points of nondifferentiability, we show that both conditions
are equivalent and derive a relaxed optimality system that is
amenable to numerical solution using SSN.
Livia Betz, Universität Duisburg-Essen
Strong stationarity for the optimal control of non-smooth
coupled systems
This talk is concerned with an optimal control problem governed by a non-smooth coupled system of equations. The
non-smooth nonlinearity is Lipschitz-continuous and directionally differentiable, but not Gâteaux-differentiable. We derive astrong stationary optimality system, i.e., an optimality system
which is equivalent to the purely primal optimality condition
saying that the directional derivative of the reduced objective
in feasible directions is nonnegative.

We study the consistent numerical approximation of optimal
boundary control problems for scalar hyperbolic conservation
laws. We consider conservative finite difference schemes withcorresponding sensitivity and adjoint schemes. To analyze theconvergence of the adjoint schemes, we propose two convenientcharacterizations of reversible solutions for the adjoint equationin the case of boundary control, which are suitable to show thatthe limit of discrete adjoints is in fact the correct reversible
solution.
Laurent Pfeiffer, University of Graz
Analysis of the Receding Horizon Control method for stabilization problems
The talk is dedicated to the analysis of the Receding-Horizon
Control (RHC) method for a class of infinite dimensional stabilisation problems. An error estimate, bringing out the effectof the prediction horizon, the sampling time, and the terminal
cost used for the truncated problems will be provided.
Reference: Karl Kunisch and Laurent Pfeiffer. The effect of
the Terminal Penalty in Receding Horizon Control for a Classof Stabilization Problems. ArXiv preprint, 2018.
Caroline Löbhard, Weierstrass Institute for Applied Analysis
and Stochastics (WIAS)Space-time discretization for parabolic optimal control
problems with state constraints
The first order system of a regularized version of the control
problem is reformulated as an optimality condition in termsof only the state variable, which renders an elliptic boundary
value problem of second order in time, and of fourth orderin space. We suggest a C 0 interior penalty method on thespace-time domain with tailored terms that enforce suitable
regularity in space direction. A combined numerical analysis
leads to a solution algorithm that intertwines mesh refinementwith an update strategy for the regularization parameter.

Wed.2 14:15–15:30

Rate independent systems can be formulated based on anenergy functional and a dissipation potential that is assumed tobe convex, lower semicontinuous and positively homogeneous ofdegree one. Here, we will focus on the nonconvex case meaningthat the energy is not convex. In this case, the solution typicallyis discontinuous in time. There exist several (in general not
equivalent) notions of weak solutions. We focus on so-calledbalanced viscosity solutions, discuss the properties of solution
sets and discuss the well posedness of an optimal control
problem for such systems.

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PDE
Wed.2 H 0112
Decomposition-Based Methods for Optimization of TimeDependent Problems (Part I)
Organizers: Matthias Heinkenschloss, Carl Laird
Chair: Carl Laird

ROB
Wed.2 H 3006
Recent Advances in Theories of Distributionally Robust
Optimization (DRO)
Organizer: Zhichao Zheng
Chair: Zhichao Zheng

Sebastian Götschel, Zuse Institute Berlin (joint work withMichael L. Minion)
Towards adaptive parallel-in-time optimization of parabolic
PDEs

Karthyek Murthy, Singapore University of Technology and Design (joint work with Karthik Natarajan, Divya Padmanabhan)
Exploiting Partial Correlations in Distributionally Robust
Optimization

Time-parallel methods have received increasing interest in
recent years as they allow to obtain speedup when spatial parallelization saturates. In this talk, we exploit the state-of-theart “Parallel Full Approximation Scheme in Space and Time”(PFASST) for the time-parallel solution of optimization problems with parabolic PDEs. As PFASST is based on spectral
deferred correction methods their iterative nature provides additional flexibility, e.g., by re-using previously computed solutions.
We focus especially on adapting the accuracy of state andadjoint solves to the optimization progress.

We aim to identify partial covariance structures that allow
polynomial-time instances for evaluating the worst-case expected value of mixed integer linear programs with uncertain
coefficients. Assuming only the knowledge of mean and covariance entries restricted to block-diagonal patterns, we develop
a reduced SDP formulation whose complexity is characterized
by a suitable projection of the convex hull of rank 1 matrices
formed by extreme points of the feasible region. These projected constraints lend themselves to polynomial-time instances
in appointment scheduling and PERT network contexts.

Stefan Ulbrich, TU Darmstadt
Time-domain decomposition for PDE-constrained optimization

Jingui Xie, University of Science and Technology of China (joint
work with Melvyn Sim, Taozeng Zhu)
Joint Estimation and Robustness Optimization

We consider optimization problems governed by timedependent parabolic PDEs and discuss the construction ofparallel solvers based on time-domain decomposition. We propose an ADMM-based approach to decompose the problem
in independent subproblems and combine it with a multigrid
strategy. Moreover, we discuss a preconditioner for the solutionof the subproblems that decouples into a forward and backward PDE solve on the time-subdomains. Numerical results
are presented.

Many real-world optimization problems have input parameters estimated from data whose inherent imprecision can leadto fragile solutions that may impede desired objectives and/or
render constraints infeasible. We propose a joint estimation
and robustness optimization (JERO) framework to mitigate
estimation uncertainty in optimization problems by seamlessly
incorporating both the parameter estimation procedure andthe optimization problem.

Wed.2 14:15–15:30

Denis Ridzal, Sandia National Laboratories (joint work with
Eric Cyr)A multigrid-in-time augmented-Lagrangian method forconstrained dynamic optimization
To address the serial bottleneck of forward and adjoint time
integration in solving dynamic optimization problems, we propose a parallel multigrid-in-time technique for the solutionof optimality systems in a matrix-free trust-region sequential
quadratic programming method for equality-constrained optimization. Additionally, to handle general inequality constraints,
we develop and analyze an augmented-Lagrangian extension,
which only augments simple bound constraints. We present
scaling studies for large-scale fluid dynamics control problems
and inverse problems involving Maxwell’s equations.

Grani A. Hanasusanto, The University of Texas at Austin (joint
work with Guanglin Xu)
Improved Decision Rule Approximations for Multi-Stage
Robust Optimization via Copositive Programming
We study decision rule approximations for multi-stage robust
linear optimization problems. We examine linear decision rules
for the generic problem instances and quadratic decision rules
for the case when only the problem right-hand sides are uncertain. The decision rule problems are amenable to exact copositive programming reformulations that yield tight, tractable
semidefinite programming solution schemes. We prove thatthese new approximations are tighter than the state-of-the-art
schemes and demonstrate their superiority through numericalexperiments.

Parallel Sessions Abstracts Wed.2 14:15–15:30

Anirudh Subramanyam, Argonne National Laboratory (joint
work with Chrysanthos Gounaris, Nikolaos Lappas)
A Generalized Cutting Plane Algorithm for Robust Optimization with Decision-dependent Uncertainty Sets
We propose a primal branch-and-bound algorithm that generalizes the well-known cutting plane method of Mutapcic andBoyd to the case of decision-dependent uncertainty sets. A
comprehensive computational study showcases the advantagesof the proposed algorithm over that of existing methods that
rely on classical duality-based reformulations.
Mathieu Besançon, INRIA Lille Nord-Europe (joint work with
Miguel Anjos, Luce Brotcorne)
Near-optimal robust bilevel optimization
Bilevel optimization corresponds to integrating the optimal
response to a lower-level problem in a problem. We introduce
near-optimal robustness for bilevel problems, building on existing concepts from the literature. This model generalizes
the pessimistic approach, protecting the decision-maker fromlimited deviations of the lower level. This models finds intuitive
interpretations in various optimization settings modelled as
bilevel problems. We develop a duality-based solution method
for cases where the lower-level is convex, leveraging the methodology from robust and bilevel optimization.
Omid Nohadani, Northwestern University (joint work with Kartikey Sharma)
Optimization Under Decision-dependent Uncertainty
The efficacy of robust optimization spans a variety of settings with predetermined uncertainty sets. In many applications,
uncertainties are affected by decisions and cannot be modeledwith current frameworks. This talk takes a step towards generalizing robust optimization to problems with decision-dependent
uncertainties, which we show are NP-complete. We introduce aclass of uncertainty sets whose size depends on decisions, andproposed reformulations that improve upon alternative techniques. In addition, the proactive uncertainty control mitigatesover conservatism of current approaches.

SPA
Wed.2 H 2038
Primal-Dual Methods for Structured Optimization
Organizer: Mathias Staudigl
Chair: Mathias Staudigl
Robert Ernö Csetnek, University of Vienna
The proximal alternating minimization algorithm for twoblock separable convex optimization problems with linear
constraints
The Alternating Minimization Algorithm has been proposed
by Paul Tseng to solve convex programming problems with
two-block separable linear constraints and objectives, whereby(at least) one of the components of the latter is assumed to
be strongly convex. The fact that one of the subproblems tobe solved within the iteration process of this method does not
usually correspond to the calculation of a proximal operator
through a closed formula affects the implementability of thealgorithm. In this talk, we allow in each block of the objective afurther smooth convex function and propose a proximal versionof the algorithm, which is achieved by equipping the algorithmwith proximal terms induced by variable metrics. For suitablechoices of the latter, the solving of the two subproblems in theiterative scheme can be reduced to the computation of proximal
operators. We investigate the convergence of the proposed
algorithm in a real Hilbert space setting and illustrate its
numerical performances on two applications in image processing
and machine learning.
Darina Dvinskikh, Weierstrass Institute for Applied Analysis
and Stochastics (WIAS)
Complexity rates for accelerated primal-dual gradientmethod for stochastic optimisation problem
We study the optimal complexity bounds of first-order methods for the convex finite-sum minimization problem. To address
this problem, we appeal to distributed optimization and consider
the network of multiple agents (processors) which cooperatively minimize the target function. We compare two different
approaches toward the finding of the minimum depending on
the functions properties in terms of oracle calls and communication rounds of distributed system: primal approach and dual
approach which is based on the assumption of existence of the
Fenchel-Legendre transform for the objective.
Kimon Antonakopoulos, University Grenoble Alpes/Inria (joint
work with Panayotis Mertikopoulos)
Primal-dual methods for Stochastic Variational Inequalities with Unbounded Operators
Motivated by applications in machine learning and imaging,
we study a class of variational inequality problems with possibly
unbounded operators. In contrast to their classical counterparts,
these problems could exhibit singularities near the boundaryof the problem domain, thus precluding the use of standard
first-order methods. To circumvent this difficulty, we introduce
a family of local norms adapted to the problem’s singularity
landscape, and we derive a class of Bregman proximal methods
that exploit this structure and provide optimal convergence
rates in this setting.

Wed.2 14:15–15:30

ROB
Wed.2 H 3007
Decision-Dependent Uncertainty and Bi-Level Optimization
Organizers: Chrysanthos Gounaris, Anirudh Subramanyam
Chair: Chrysanthos Gounaris

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STO
Wed.2 H 3012
Stochastic Optimization and Its Applications (Part II)
Organizer: Fengmin Xu
Chair: Fengmin Xu
Sunho Jang, Seoul National University (joint work with Insoon
Yang)
Stochastic Subgradient Methods for Dynamic Programming in Continuous State and Action Spaces
To solve dynamic programs with continuous state and action
spaces, we first approximate the output of the Bellman operator
as a convex program with uniform convergence. This convex
program is then solved using stochastic subgradient descent. To
avoid the full projection onto the high-dimensional feasible set,
we develop an algorithm that samples, in a coordinated fashion,a mini-batch for a subgradient and another for projection. We
show several properties of this algorithm, including convergence,
and a reduction in the feasibility error and in the variance ofthe stochastic subgradient.
Jussi Lindgren, Aalto University School of Science (joint workwith Jukka Liukkonen)
Stochastic optimization, Quantum Dynamics and Information Theory
We present a new approach to foundations of quantum
mechanics via stochastic optimization. It is shown that the
Schrödinger equation can be interpreted as an optimal dynamic
for the value function for a coordinate-invariant linear-quadratic
stochastic control problem. The optimal path for the quantum
particle obeys a stochastic gradient descent scheme, which
minimizes relative entropy or the free energy. This in turn allows
for a new interpretation for quantum mechanics through thetools of non-equilibrium statistical mechanics and information
theory vis-a-vis the path integral formulation.

Wed.3 16:00–17:15

Luis Espath, King Abdullah University of science and technology (KAUST) (joint work with Andre Carlon, Ben Dia, Rafael
Lopez, Raul Tempone)
Nesterov-aided Stochastic Gradient Methods using
Laplace Approximation for Bayesian Design Optimization
We focus on the Optimal Experimental Design OED problemof finding the setup that maximizes the expected information
gain. We augment the stochastic gradient descent with the
Nesterov’s method and the restart technique, as O’Donoghue
and Candes originally proposed for deterministic optimization.
We couple the stochastic optimization with three estimators
for OED: the double-loop Monte Carlo estimator DLMC, theMonte Carlo estimator using the Laplace approximation for theposterior distribution MCLA and the double-loop Monte Carloestimator with Laplace-based importance sampling DLMCIS.

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131

Wed.3 16:00–17:15

Daniel Ralph, University of Cambridge (joint work with RutgerJan Lange, Kristian Støre)“POST”, a robust method for Real-Option Valuation in
Multiple Dimensions Using Poisson Optional Stopping
Times

BIG
Wed.3 H 0104
Recent Advancements in Optimization Methods for Machine Learning (Part IV)
Organizers: Albert Berahas, Martin Takáč
Chair: Albert BerahasMartin Jaggi, EPFL
Decentralized Learning with Communication Compression

We provide a new framework for valuing multidimensional
real options where opportunities to exercise the option are generated by an exogenous Poisson process; this can be viewed asa liquidity constraint on decision times. This approach, which
we call the Poisson optional stopping times (POST) method,
finds the value function as a monotone and linearly convergent
sequence of lower bounds. In a case study, we demonstrate
the effectiveness of POST by comparing it to the frequently
used quasi-analytic method, showing some difficulties that arise
when using the latter. POST has a clear convergence analysis,
is straightforward to implement and robust in analysing multidimensional option-valuation problems of modest dimension.

We consider decentralized stochastic optimization, as intraining of machine learning and deep learning models, where
the training data remains separated over many user devices. We
propose a new communication-efficient decentralized algorithm
based on gradient compression (sparsification and quantization)for SGD as in https://arxiv.org/abs/1902.00340 , while also providing faster consensus algorithms under communication compression. Finally we discuss flexible decentralized learning of generalized linear models as in https://arxiv.org/abs/1808.0488

Peter Schütz, Norwegian University of Science and Technology
NTNU (joint work with Sjur Westgaard)
Optimal hedging strategies for salmon producers

Wouldn’t it be great if there was automatic way of setting
the optimal mini-batch size? In this talk I will present a rather
general approach for choosing the optimal mini-batch size
based on how smooth is the mini-batch function. For this I will
introduce the notation of expected smoothness, and show our
we are using this notation to choose the mini-batch size andstepsize in SVRG, SGD, and SAG. I will most likely focus on
SVRG.

We use a multistage stochastic programming model to study
optimal hedging decisions for risk-averse salmon producers.
The objective is to maximize the weighted sum of expected
revenues from selling salmon either in the spot market or in
futures contracts and Conditional Value-at-Risk (CVaR) of
the revenues over the planning horizon. The scenario tree
for the multistage model is generated based on a procedurethat combines Principal Component Analysis and state spacemodelling. Results indicate that salmon producers should hedge
price risk already at fairly low degrees of risk aversion.
Luis Zuluaga, Lehigh University (joint work with Onur Babat,
Juan Vera)
Computing near-optimal Value-at-Risk portfolios using integer programming techniques
Value-at-Risk (VaR) is one of the main regulatory tools
used for risk management purposes. However, it is difficult tocompute optimal VaR portfolios; that is, an optimal risk-reward
portfolio allocation using VaR as the risk measure. This is due
to VaR being non-convex and of combinatorial nature. Here, we
present an algorithm to compute near-optimal VaR portfoliosthat takes advantage of a MILP formulation of the problem
and provides a guarantee of the solution’s near-optimality.

Robert Gower, Telecom Paris Tech
Expected smoothness is the key to understanding the minibatch complexity of stochastic gradient methods

Ahmet Alacaoglu, EPFL (joint work with Volkan Cevher, Olivier
Fercoq, Ion Necoara)
Almost surely constrained convex optimization
We propose a stochastic gradient framework for solving
stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need
to be satisfied almost surely. We use smoothing and homotopy techniques to handle constraints without the need for
matrix-valued projections. We show for our stochastic gradientalgorithm optimal rates up to logarithmic factors even without
constraints, for general convex and restricted strongly convex
problems. We demonstrate the performance of our algorithmwith numerical experiments.

Wed.3 16:00–17:15

APP
Wed.3 H 2053
Applications in Finance and Real Options
Chair: Luis Zuluaga

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BIG
Wed.3 H 0110
Recent Advances in Convex and Non-Convex Optimization
and Their Applications in Machine Learning (Part II)
Organizers: Qihang Lin, Tianbao Yang
Chair: Tianbao Yang
Yuejie Chi, Carnegie Mellon University
Implicit Regularization in Nonconvex Low-Rank Matrix
Completion
We show that factored gradient descent with spectral initialization converges linearly for noisy and 1-bit low-rank matrix
completion in terms of both Frobenius and infinity norms, without the need of explicitly promoting incoherence via regularizations, at near-optimal sample and computational complexities.
This is achieved by establishing an “implicit regularization”
phenomenon of the algorithm via a leave-one-out perturbationargument.
Praneeth Netrapalli, Microsoft Corporation (joint work withChi Jin, Michael Jordan)
Nonconvex-Nonconcave Minmax Optimization: Stable
Limit Points of Gradient Descent Ascent are Locally Optimal
Minimax optimization, especially in its general nonconvexnonconcave formulation, has found extensive applications inmodern machine learning frameworks. While gradient descentascent (GDA) is widely used in practice to solve these problems
its theoretical behavior has been considered highly undesirable
due to the possibility of convergence to non local-Nash equilibria. In this talk, we introduce the notion of local minimax as
a more suitable alternative to the notion of local Nash equilibrium and show that up to some degenerate points, stable limit
points of GDA are exactly local minimax.
Raef Bassily, The Ohio State University (joint work with
Mikhail Belkin)
On the Effectiveness of SGD in Modern Overparameterized Machine Learning
Wed.3 16:00–17:15

I will talk about recent advances in understanding Stochastic Gradient Descent and its effectiveness in modern machinelearning. I will discuss linear convergence of SGD for a broadclass of convex as well as non-convex problems in the interpolated (over-parametrized) regime. I will also discuss some
practical and theoretical implications pertaining to training
neural networks.

BIG
Wed.3 H 3002
Recent Advances in Distributed Optimization
Organizers: Pavel Dvurechensky, Cesar A Uribe
Chair: Cesar A Uribe
Alex Olshevsky, Boston University (joint work with Ioannis
Paschalidis, Artin Spiridonoff)
Asymptotic Network Independence for Stochastic Gradient Descent
We consider stochastic gradient descent in a network ofn nodes: every node knows a strongly convex function, andthe goal is to compute the minimizer of the sum of thesen functions. At each time step, every node can broadcast a
message to its neighbors in a (directed) network and query its
own function for a noisy gradient. We describe a method which
has the property that we call asymptotic network independence:in the limit, the distributed system converges to the global
minimizer as fast as a centralized method that can obtain allof the n of the noisy gradients at every time step.
Alexander Gasnikov, Moscow Institute of Physics and Technology (joint work with Darina Dvinskikh, Pavel Dvurechensky,
Alexey Kroshnin, Nazarii Tupitsa, Cesar A Uribe)
On the Complexity of Approximating Wasserstein Barycenters
We study two distributed algorithms for approximating
Wasserstein barycenter of a set of discrete measures. The
first approach is based on the Iterative Bregman Projections(IBP) algorithm for which our novel analysis gives a complexity
bound proportional to mn2 /e2 to approximate the original
non-regularized barycenter. Using an alternative acceleratedgradient-descent-based approach, we obtain a complexity proportional to mn2.5 /e.
Jingzhao Zhang, (joint work with Ali Jadbabaie, Aryan
Mokhtari, Suvrit Sra, Cesar A Uribe)
Direct Runge-Kutta discretization achieves acceleration
We study gradient-based optimization methods obtained bydirectly discretizing a second-order heavy-ball ordinary differential equation (ODE). When the function is smooth enoughand convex, we show that acceleration can be achieved by astable discretization using standard Runge-Kutta integrators.
Furthermore, we introduce a new local flatness condition under which rates even faster than Nesterov’s method can beachieved with low-order integrators. Similar techniques can beapplied to strongly convex first order optimization problems
and distributed optimization problems.

Parallel Sessions Abstracts Wed.3 16:00–17:15

Olivier Huber, Humboldt-Universität zu Berlin
On Solving Models with Optimal Value Function
We look at solving mathematical programs that contain otheroptimization problems, specifically containing Optimal Value
Functions (OVF). In general, problems with OVF are feature
non-smoothness, that is both hard to model and solve. Rather
than designing specific algorithms for this kind of problem,
we propose an approach based on reformulations. In this talk
we first focus the conditions for such reformulations to be
applicable. Secondly, we present ReSHOP, an implementationof those procedures, as well as its Julia interface.
Youngdae Kim, Argonne National Laboratory
SELKIE: a model transformation and distributed solver for
structured equilibrium problems
We introduce SELKIE, a general-purpose solver for equilibrium problems described by a set of agents. It exploits problem
structures, such as block structure and cascading dependencies
of interacting agents, in a flexible and adaptable way to achievea more robust and faster solution path. Various decomposition
schemes can be instantiated in a convenient and computationally efficient manner. SELKIE has been implemented and isavailable within GAMS/EMP. Examples illustrating the flexibility and effectiveness of SELKIE are given, including a Dantzig
Wolfe method for Variational Inequalities.
Michael Ferris, University of Wisconsin-Madison (joint workwith Andy Philpott)
Capacity expansion and operations tradeoffs in renewableelectricity
Various countries have announced target dates for a 100%
renewable electricity system. Such targets require long-term
investment planning, medium-term storage management (including batteries and pumped storage), as well as a short-term
analysis of demand-response, involuntary load curtailment andtransmission congestion. We consider using complementarity
models of dynamic risked equilibria to formulate these problems, the implications of stochasticity, and demonstrate theapplication of additional modeling constructs and distributed
algorithms for their solution.

CON
Wed.3 H 0111
Theoretical Aspects of Conic Optimization
Organizer: Roland Hildebrand
Chair: Roland HildebrandNaomi Shaked-Monderer, The Max Stern Yezreel Valley College
On the generalized inverse of a copositive matrix
We take a closer look at copositive matrices satisfying aknown condition for the copositivity of the (Moore-Penrose
generalized) inverse of the matrix.
Takashi Tsuchiya, National Graduate Institute for Policy Studies (joint work with Bruno Figueira Lourenço, Masakazu Muramatsu)
Duality theory of SDP revisited: most primal-dual weakly
feasible SDPs have finite nonzero duality gaps
We analyze the duality of SDP to understand how positive
duality gaps arise. We show that there exists a relaxation of thedual whose optimal value coincides with the primal, whenever
the primal and dual are weakly feasible. The relaxed dual isconstructed by removing several linear constraints from the dual,
and therefore is likely to have a larger optimal value generically,leading to a positive duality gap. The analysis suggests that
positive duality gap is a rather generic phenomenon when boththe primal and dual are weakly feasible.
Roland Hildebrand, Laboratoire Jean Kuntzmann / CNRS
Towards optimal barriers for convex cones
Self-concordant barriers are essential for interior-point algorithms in conic programming. To speed up the convergence it isof interest to find a barrier with the lowest possible parameter
for a given cone. The barrier parameter is a non-convex function
on the set of self-concordant barriers, and finding an optimal
barrier amounts to a non-convex infinite-dimensional optimization problem. We describe this problem and show how it can beconvexified at the cost of obtaining sub-optimal solutions withguaranteed performance. Finite-dimensional approximations
yield lower bounds on the parameter.

Wed.3 16:00–17:15

VIS
Wed.3 H 1058
Complementarity: Extending Modeling and Solution Techniques (Part I)
Organizer: Michael Ferris
Chair: Michael Ferris

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CON
Wed.3 H 2032
Conic Optimization and Machine Learning
Organizer: Amir Ali Ahmadi
Chair: Amir Ali Ahmadi
Pablo Parrilo, MIT
TBA [canceled]
TBA
Pravesh Kothari, Carnegie Mellon University
Average-Case Algorithm Design Using Sum-of-Squares
I will explain how a primal-dual viewpoint on Sum-of-Squares
method and its proof-complexity interpretation provides a general blueprint for parameter estimation problems arising inmachine learning/average-case complexity. As a consequence,
we will be able to obtain state-of-the-art algorithms for basic
problems in theoretical machine learning/statistics including
estimating components of gaussian mixtures, robust estimationof moments of distributions, robust independent component
analysis and regression, tensor decomposition, tensor completion and dictionary learning.
Amir Ali Ahmadi, Princeton University (joint work with Bachir
El Khadir)
Learning Dynamical Systems with Side Information
We present a mathematical framework for learning a dynamical system from a limited number of trajectory observations
but subject to contextual information. We show that sum ofsquares optimization is a particularly powerful tool for this
task.

CNV
Wed.3 H 1028
New Frontiers in Splitting Algorithms for Optimization andMonotone Inclusions (Part III)
Organizer: Patrick L. Combettes
Chair: Patrick L. Combettes
Nimit Nimana, Khon Kaen University (joint work with Narin
Petrot)Generalized forward-backward splitting with penalty terms
for monotone inclusion problems
We introduce generalized forward-backward splitting methods with penalty terms for solving monotone inclusion problems
involving the sum of a finite number of maximally monotone
operators and the normal cone to the nonempty set of zeros ofanother maximally monotone operator. We show convergencesof the iterates, provided that the condition corresponding to
the Fitzpatrick function of the operator describing the set of
the normal cone is fulfilled. We illustrate the functionality ofthe methods through some numerical experiments
Dang-Khoa Nguyen, University of Vienna (joint work with Radu
Boţ, Robert Ernö Csetnek)
A proximal minimization algorithm for structured nonconvex and nonsmooth problems
We propose a proximal algorithm for minimizing objectivefunctions consisting of three summands: the composition of anonsmooth function with a linear operator, another nonsmoothfunction and a smooth function which couples the two block
variables. The algorithm is a full splitting method. We provide
conditions for boundedness of the sequence and prove thatany cluster point is a KKT point of the minimization problem.
In the setting of the Kurdyka-Lojasiewicz property, we show
global convergence and derive converge rates for the iterates
in terms of the Lojasiewicz exponent.
Marcel Jacobse, Universität Bremen (joint work with Christof
Büskens)
Examining failure of a Mehrotra predictor-corrector
method on minimal examples

Wed.3 16:00–17:15

Mehrotra’s predictor-corrector method for linear and convex
quadratic programming is well known for its great practical performance. Strictly speaking it does lack a convergence theory
however. As a result, there are examples for which Mehrotratype algorithms fail to converge. In the talk, divergence onparticularly small quadratic programs is examined. Different
strategies and safe guards for promoting convergence are discussed.

Parallel Sessions Abstracts Wed.3 16:00–17:15

Masoud Ahookhosh, KU Leuven (joint work with Panagiotis
Patrinos, Andreas Themelis)
A superlinearly convergent Bregman forward-backward
method for nonsmooth nonconvex optimization
A superlinearly convergent Bregman forward-backward
method for solving sum of two nonconvex functions is proposed, where the first function is relatively smooth and thesecond one is (nonsmooth) nonconvex. We first introduce the
Bregman forward-backward envelope (BFBE), verify its firstand second-order properties under some mild assumptions, anddevelop a newton-type forward-backward method using BFBE
that only needs first-order black-box oracle. After giving the
global and local superlinear convergence of the method, we will
present encouraging numerical results from several applications.
Avinash Dixit, Indian Institute of Technology (BHU) Varanasi(joint work with Tanmoy Som)A new accelerated algorithm to solve convex optimization
problems
We consider the convex minimization problems of the sumof two convex functions, in which one is differentiable. In
the present research work, we used the iterative techniqueto solve the minimization problem. The recent trend is tointroduce techniques having greater convergence speed. Firstly,
we introduce an accelerated algorithm to find minimization ofa nonexpansive mapping and corresponding proximal gradientalgorithm to solve convex optimization problems. Later on, theproposed algorithm is compared with already existing algorithms
on the basis of convergence speed and accuracy.
Ewa Bednarczuk, Warsaw University of Technology (joint workwith Krzysztof Rutkowski)[moved] On Lipschitzness of projections onto convex sets
given by systems of nonlinear inequalities and equations
under relaxed constant rank condition
When approaching convex optimization problems from continuous perspective the properties of related dynamical systems
often depend on behavior of projections, In particular, wheninvestigating continuous variants of primal-dual best approximation methods, projections onto moving convex sets appear.
We investigate lipschitzness of projection onto moving convex
sets. To this aim we use the relaxed constant rank condition,
introduced by Minchenko and Stakhovski.

DER
Wed.3 H 2033
Derivative-Free Optimization Under Uncertainty (Part II)
Organizers: Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Chair: Stefan Wild
Fani Boukouvala, Georgia Institute of Technology (joint workwith Jianyuan Zhai)
Data-driven branch-and-bound algorithms for derivativefree optimization
Use of high-fidelity simulations is common for computeraided design, but lack of analytical equations creates challenges.
One main challenge in derivative-free optimization is the variability in the incumbent solution, when (a) the problem isstochastic; (b) different initial samples or surrogate models
are used to guide the search. We propose to mitigate thislack of robustness in globally convergent behavior, through asurrogate-based spatial branch-and-bound method. We derivebounds for several surrogate models and use them to cleverly
partition and prune the space for faster convergence.
Friedrich Menhorn, Technical University of Munich (joint workwith Hans-Joachim Bungartz, Michael S. Eldred, Gianluca
Geraci, Youssef M. Marzouk)
Near-optimal noise control in derivative-free stochastic optimization
We present new developments for near-optimal noise control
in the stochastic nonlinear derivative-free constrained optimization method SNOWPAC. SNOWPAC estimates measuresof robustness or risk, appearing in the optimization objective
and/or constraints, using Monte Carlo sampling. It also employsa derivative-free trust region approach that is regularized by
the resulting sampling noise. We describe how to use Gaussian
processes as control variates to decrease this noise in a nearly
optimal fashion, thus improving the trust region approximationand the convergence of the method.
Morteza Kimiaei, University of Vienna (joint work with Arnold
Neumaier)
Efficient noisy black box optimization
We present a new algorithm for unconstrained noisy black box
optimization in high dimension, based on low rank quadraticmodels for efficiency, and on techniques for guaranteeing goodcomplexity results. A comparison of our algorithm with somesolvers from the literature will be given.

Wed.3 16:00–17:15

CNV
Wed.3 H 3025
Algorithms for Large-Scale Convex and Nonsmooth Optimization (Part II)
Chair: Daniel Robinson

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GLO

NON

Wed.3 H 3008
Generalized Distances and Envelope Functions
Organizer: Ting Kei Pong
Chair: Ting Kei Pong

Wed.3 H 0105
Geometry and Optimization
Organizer: Coralia Cartis
Chair: Coralia Cartis

Scott B. Lindstrom, The Hong Kong Polytechnic University(joint work with Regina S. Burachik, Minh N. Dao)
The Fitzpatrick Distance

Florentin Goyens, University of Oxford
Nonlinear matrix recovery

Recently, Burachik and Martinez-Legaz introduced a distanceconstructed from a representative function for a maximallymonotone operator. This distance generalizes the Bregman
distance, and we name it the Fitzpatrick distance. We explore
the properties of its variants.
Andreas Themelis, KU Leuven (joint work with Panagiotis Patrinos)
A universal majorization-minimization framework for theconvergence analysis of nonconvex proximal algorithms
We provide a novel unified interpretation of nonconvex splitting algorithms as compositions of Lipschitz and set-valued
majorization-minimization mappings. A convergence analysis
is established based on proximal envelopes, a generalizationof the Moreau envelope. This framework also enables the integration with fast local methods applied to the nonlinear
inclusion encoding optimality conditions. Possibly under assumptions to compensate the lack of convexity, this setting is
general enough to cover ADMM as well as forward-backward,
Douglas-Rachford and Davis-Yin splittings.
Ting Kei Pong, The Hong Kong Polytechnic University (joint
work with Michael Friedlander, Ives Macedo)
Polar envelope and polar proximal map

Wed.3 16:00–17:15

Polar envelope is a convolution operation specialized togauges, and is analogous to Moreau envelope. In this talk,we discuss important properties of polar envelope and thecorresponding polar proximal map. These include smoothnessof the envelope function, and uniqueness and continuity ofthe proximal map. We also highlight the important roles thepolar envelope plays in gauge duality and the construction ofalgorithms for gauge optimization.

We investigate the recovery of a partially observed highrank matrix whose columns obey a nonlinear structure. The
structures covered are unions of clusters and algebraic varieties,
which include unions of subspaces. Using a nonlinear lifting toa space of features, as proposed by Ongie et al. in 2017, wereduce the problem to a constrained nonconvex optimization
formulation, which we solve using Riemannian optimization
methods. We give theoretical convergence and complexity
guarantees and provide encouraging numerical results for low
dimensional varieties and clusters.
Erlend Skaldehaug Riis, University of Cambridge
A geometric integration approach to nonsmooth, nonconvex optimisation
Discrete gradient methods are popular numerical methods
from geometric integration for solving systems of ODEs. They
are known for preserving structures of the continuous system,e.g. energy dissipation, making them interesting for optimisation problems. We consider a derivative-free discrete gradientapplied to dissipative ODEs such as gradient flow, thereby
obtaining optimisation schemes that are implementable in a
black-box setting and retain favourable properties of gradient flow. We give a theoretical analysis in the nonsmooth,nonconvex setting, and conclude with numerical results.
Mario Lezcano Casado, University of Oxford
Optimization in compact matrix Lie groups, with applications to machine learning
We present and analyze a novel approach to perform firstorder optimization with orthogonal and unitary constraints.
This approach is based on a parametrization stemming fromLie group theory through the exponential map. The parametrization transforms the constrained optimization problem into an
unconstrained one over a Euclidean space, for which common
first-order optimization methods can be used. The theoreticalresults presented are general enough to cover the special orthogonal group, the unitary group and, in general, any connectedcompact Lie group.

Parallel Sessions Abstracts Wed.3 16:00–17:15

Nikitas Rontsis, University of Oxford (joint work with Paul
Goulart, Yuji Nakatsukasa)
An eigenvalue based active-set solver for norm-constrained
quadratic problems, with applications to extended trust
region subproblems and sparse PCA
We present an active-set algorithm for the minimisation ofa non-convex quadratic function subject to linear constraints
and a single norm constraint. The suggested algorithm works
by solving a series of Trust Region Subproblems (TRS) the
global solution of which has been studied extensively in theliterature. We extend these results by showing that non-global
minima of the TRS, or a certificate of their absence, can bealso calculated efficiently by solving a single eigenproblem. We
demonstrate the usefulness of the algorithm in solving Sparse
PCA and extended Trust Region Subproblems
André Uschmajew, Max Planck Institute for Mathematics in
the Sciences (joint work with Felix Krahmer, Max Pfeffer)
A Riemannian optimization approach to sparse low-rank
recovery
The problem of recovering a row or column sparse low-rankmatrix from linear measurements arises for instance in sparseblind deconvolution. We consider non-convex optimization algorithms for such problems that combine the idea of an ADMM
method for sparse recovery with Riemannian optimization on
the low-rank manifold and thresholding.
Tianxiang Liu, RIKEN Center for Advanced Intelligence Project(AIP) (joint work with Ivan Markovsky, Ting Kei Pong, Akiko
Takeda)A successive difference-of-convex approximation methodwith applications to control problems
In this talk, we consider a class of nonconvex nonsmooth
optimization problems whose objective is the sum of a smoothfunction and several nonsmooth functions, some of which arecomposed with linear maps. This includes the problems arisingfrom system identification with multiple rank-constraints, eachof which involves Hankel structure. To solve it, we propose a
successive DC approximation method, which makes use of the
DC structure of the Moreau envelope. We prove the convergence of the method under suitable assumptions and discuss
how it can be applied to concrete applications.

NON
Wed.3 H 1029
Advances in Nonlinear Optimization Algorithms (Part II)
Chair: Mehiddin Al-Baali
Erik Berglund, KTH Royal University of Technology (joint workwith Mikael Johansson)
An Eigenvalue Based Limited Memory BFGS Method
Many matrix update formulas in quasi-Newton methods canbe derived as solutions to nearest-matrix problems. In thistalk, a similar idea is proposed for developing limited memory
quasi-Newton methods. We characterize a class of symmetricmatrices that can be stored with limited memory. We thenderive an efficient way to find the closest member of this classto any symmetric matrix and propose a trust region methodthat makes use of it. Numerical experiments show that ouralgorithm has superior performance on large logistic regression
problems, compared to a regular L-BFGS trust region method.
Wenwen Zhou, SAS Institute Inc. (joint work with Joshua Griffin, Riadh Omheni)
A modified projected gradient method for bound constrained optimization problems
This talk will focus on solving bound constrained nonconvex
optimization problems with expensive-to-evaluate functions.
The motivation for this research was from collaborations withmembers from the statistics community seeking better handlingof their optimization problems. The method first solves a QP
using the projected gradient method to improve the active set
estimate. The method next uses a modified conjugate gradientmethod applied to the corresponding trust region subproblem
to leverage second order information. The numerical results
will be provided.
Hao Wang, ShanghaiTech University
An inexact first-order method for constrained nonlinear
optimization
We propose inexact first-order methods for solving constrained nonlinear optimization problems. By controlling theinexactness of the subproblem solution, we can significantly
reduce the computational cost needed for each iteration. A
penalty parameter updating strategy during the subproblem
solve enables the algorithm to automatically detect infeasibility.
Global convergence for both feasible and infeasible cases areproved. Complexity analysis for the KKT residual is also derived
under loose assumptions. Performance of proposed methods isdemonstrated by numerical experiments.

Wed.3 16:00–17:15

NON
Wed.3 H 1012
Developments in Structured Nonconvex Optimization(Part II)
Organizers: Jun-ya Gotoh, Yuji Nakatsukasa, Akiko Takeda
Chair: Jun-ya Gotoh

137

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NON
Wed.3 H 2013
New Trends in Optimization Methods and Machine Learning (Part III)
Chair: El houcine Bergou
Saeed Ghadimi, Princeton University (joint work with Krishnakumar Balasubramanian)
Zeroth-order Nonconvex Stochastic Optimization: Handling Constraints, High-Dimensionality, and Saddle-Points

NON
Wed.3 H 3007
Recent Trends in Large Scale Nonlinear Optimization(Part II)
Organizers: Michal Kočvara, Panos Parpas
Chair: Panos Parpas
Filippo Pecci, Imperial College London (joint work with Panos
Parpas, Ivan Stoianov)Non-linear inverse problems via sequential convex optimization

We analyze zeroth-order stochastic approximation algorithms
for nonconvex optimization with a focus on addressing constrained optimization, high-dimensional setting, and saddlepoint avoiding. In particular, we generalize the conditional
stochastic gradient method under zeroth-order setting andalso highlight an implicit regularization phenomenon where
the stochastic gradient method adapts to the sparsity of the
problem by just varying the step-size. Furthermore, we provide
a zeroth-order variant of the cubic regularized Newton methodand discuss its rate of convergence to local minima.

We focus on inverse problems for physical systems subject toa set of non-linear equations and formulate a generic sequential
convex optimization solution method. The proposed algorithm
is implemented for fault estimation in water supply networks.
Previous literature has solved this combinatorial problem using
evolutionary approaches. We show that the considered problem
can be approximated by a non-linear inverse problem with L1
regularization and solved using sequential convex optimization.
The developed method is tested using a large operational water
network as case study.

Hien Le, University of Mons (joint work with Nicolas Gillis,
Panagiotis Patrinos)
Inertial Block Mirror Descent Method for Non-convex
Non-smooth Optimization

Michal Kočvara, University of Birmingham (joint work with
Chin Pang Ho, Panos Parpas)
On Newton-type multilevel optimization methods

We propose inertial versions of block coordinate descentmethods for solving nonconvex nonsmooth composite optimization problems. Our methods do not require a restarting
step, allow using two different extrapolation points, and takeadvantage of randomly shuffled. To prove the convergence of
the whole generated sequence to a critical point, we modify
the well-known proof recipe of Bolte, Sabach and Teboulle,
and incorporate it with using auxiliary functions. Applied tosolve nonnegative matrix factorization problems, our methods
compete favourably with the state-of-the-art NMF algorithms.

Building upon multigrid methods, a framework of multilevel
optimization methods was developed to solve structured optimization problems, including problems in optimal control,
image processing, etc. In this talk, we give a broader viewof the multilevel framework and establish some connections
between multilevel algorithms and the other approaches. By
studying a case study of the so-called Newton-type multilevel
model, we take the first step to show how the structure of optimization problems could improve the convergence of multilevel
algorithms.
Man Shun Ang, UMONS (joint work with Nicolas Gillis)
Accelerating Nonnegative-X by extrapolation, where X ∈Least Square, Matrix Factorization, Tensor Factorization

Wed.3 16:00–17:15

Non-negativity constraint is ubiquitous. A family of nonnegativity constrained problems (NCP) are the non-negative
least squares (NNLS), the non-negative matrix factorization(NMF) and the non-negative tensor factorization (NTF). The
goal of these NCP problems in general is to fit an given object(vector, matrix, or tensor) by solving an optimization problem
with non-negativity constraints. Various algorithms are proposed to solve the NCP. In this talk, we introduce an general
extrapolation-based acceleration scheme that that can significantly accelerate the algorithm for NNLS, NMF and NTF.

Parallel Sessions Abstracts Wed.3 16:00–17:15

PDE

139

PDE

Wed.3 H 0106
Optimal Control of Nonsmooth Systems (Part III)
Organizers: Constantin Christof, Christian Clason
Chair: Christian Clason

Wed.3 H 0107
Optimal Control and Dynamical Systems (Part VII)
Organizers: Cristopher Hermosilla, Michele Palladino
Chair: Michele Palladino

Gerd Wachsmuth, BTU Cottbus-Senftenberg (joint work with
Constantin Christof)
Second-order conditions for optimal control of the obstacle
problemEmilio Vilches, Universidad de O’Higgins
Some results on the optimal control of implicit sweeping
processes

Anne-Therese Rauls, TU Darmstadt (joint work with Stefan
Ulbrich)Subgradient Calculus for the Obstacle Problem
In this talk, we discuss differentiability properties of a general class of obstacle problems and characterize generalized
derivatives from the Bouligand subdifferential of the solution
operator for the obstacle problem in all points of its domain.
The subgradients we obtain are determined by solution operators of Dirichlet problems on quasi-open domains. To use
the derived subgradients in practice within nonsmooth optimization methods, a discretization of the obstacle problem isnecessary. We investigate how the respective subgradients canbe approximated in this case.
Georg Müller, Universität Konstanz (joint work with Constantin
Christof)
Multiobjective Optimal Control of a Non-Smooth SemiLinear Elliptic PDE
The understanding of and the techniques for dealing with nonsmoothness in scalar optimization with PDEs have increased
drastically over the last years. In multiobjective non-smooth
optimization, however, few results are known.
This talk addresses the multiobjective optimal control of anon-smooth semi-linear elliptic PDE with max-type nonlinearity.
First results are focused on first order optimality condition basedon multiple adjoint equations. Additionally, we discuss thenumeric characterization of the Pareto front for few objectives
using scalarization techniques and regularization.

In this talk, we present some results on the optimal control of implicit sweeping processes such as characterizationof nonsmooth Lyapunov pairs and parametric stability. Someapplications to implicit evolution variational inequalities are
given.
Cristopher Hermosilla, Universidad Tecnica Federico Santa
Maria
Hamilton-Jacobi-Bellman approach for optimal control
problems of sweeping processes
This talk is concerned with a state constrained optimal
control problem governed by a perturbed Moreau’s sweeping
process. The focus of the talk is on the Bellman approach
for an infinite horizon problem. In particular, we focus on theregularity of the value function and on the Hamilton-JacobiBellman equation it satisfies. We discuss a uniqueness result
and we make a comparison with standard state constrained
optimal control problem to highlight a regularizing effect thatthe sweeping process induces on the value function.
Fernando Lobo Pereira, Universidade do Porto Faculdade deEngenharia (joint work with Ismael Pena, Geraldo Nunes Silva)
A Predictive Control Framework for the Sustainable Management of Resources
This article concerns a predictive control framework to support decision-making in the context of resources managementin which sustainability is achieved by deconflicting short-term– usually profit-seeking – goals of multiple agents, and thegoal of seeking the the collective equilibrium in the sense ofpreserving the required production efficiency level.
This class of problems arise in a very large spectrum ofcontexts such as agriculture, oceanic biomass exploitation,
manufacturing, and services systems. In each one of thesecontexts, the competition among the multiple decision-makers
often entails that each one adopts control strategies maximizing
his/her profit in the short term, and, as a consequence, along term depletion of the ingredients required to sustain the
production efficiency of the overall system.
Given the usual complexity of any realistic scenario, we consider an abstract context clarifying the core mathematical issuesof a bi-level control architecture that, under some reasonablegeneral assumptions, generates control strategies ensuring theasymptotic convergence to a sustainable global equilibrium
while ensuring the economic (and, thus, social) short term
sustainability.

Wed.3 16:00–17:15

In this talk, we consider second-order optimality conditions
for the optimal control of the obstacle problem. In particular,
we are interested in sufficient conditions which guarantee thelocal/global optimality of the point of interest. Our analysis
extends and refines existing results from the literature. We
present three counter-examples which illustrate that rather
peculiar effects can occur in the analysis of second-order optimality conditions for optimal control problems governed by
the obstacle problem.

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PDE
Wed.3 H 0112
Decomposition-Based Methods for Optimization of TimeDependent Problems (Part II)
Organizers: Matthias Heinkenschloss, Carl Laird
Chair: Matthias Heinkenschloss
Nicolas Gauger, TU Kaiserslautern (joint work with Eric Cyr,
Stefanie Günther, Lars Ruthotto, Jacob B. Schroder)
Layer-parallel training of deep residual neural networks
Residual neural networks (ResNets) are a promising class ofdeep neural networks that have shown excellent performancefor a number of learning tasks, e.g., image classification andrecognition. Mathematically, ResNet architectures can be interpreted as forward Euler discretizations of a nonlinear initial value
problem whose time-dependent control variables represent theweights of the neural network. Hence, training a ResNet can becast as an optimal control problem of the associated dynamicalsystem. For similar time-dependent optimal control problems
arising in engineering applications, parallel-in-time methods
have shown notable improvements in scalability. In this talk,we demonstrate the use of those techniques for efficient andeffective training of ResNets. The proposed algorithms replace
the classical (sequential) forward and backward propagation
through the network layers by a parallel nonlinear multigrid
iteration applied to the layer domain. This adds a new dimension of parallelism across layers that is attractive when training
very deep networks. From this basic idea, we derive multiple
layer-parallel methods.The most efficient version employs asimultaneous optimization approach where updates to the network parameters are based on inexact gradient information inorder to speed up the training process. Using numerical examples from supervised classification, we demonstrate that the
new approach achieves similar training performance to traditional methods, but enables layer-parallelism and thus provides
speedup over layer-serial methods through greater concurrency.

Wed.3 16:00–17:15

Alexander Engelmann, Karlsruhe Institute of Technology (joint
work with Timm Faulwasser)
Decomposition of optimal control problems using bi-level
distributed ALADIN
We explore the decomposition of optimal control problems using a recently proposed decentralized variant of the Augmented
Lagrangian Alternating Direction Inexact Newton (ALADIN)
method. Specifically we consider a bi-level distributed variantof ALADIN, wherein the outer ALADIN structure is combinedwith a second inner level of distribution handling the coupling
QP by means of decentralized ADMM or similar algorithms.
We draw upon case studies from energy systems and frompredictive control to illustrate the efficacy of the proposed
framework.
Carl Laird, Sandia National Laboratories (joint work withMichael Bynum, Bethany Nicholson, Jose Santiago Rodriguez,
John Siirola, Victor Zavala)
Schur-complement and ADMM approaches for TimeDomain Decomposition in Optimization with PyNumero
PyNumero is a Python package based on Pyomo that supports development of numerical optimization algorithms. Using MPI, PyNumero has been used to develop decomposition
algorithms for structured nonlinear programming problems, including problem-level decomposition approaches (e.g., ADMM)
and approaches that decompose the linear algebra in the inner
SQP step. In this presentation, we will show computational performance of Schur-complement based approaches and ADMM
approaches on with time-domain decomposition for dynamic
optimization problems.

ROB
Wed.3 H 3004
Robust Nonlinear Optimization
Organizer: Frans de Ruiter
Chair: Frans de Ruiter
Oleg Kelis, Technion -Israel Instutute of Tecnology (joint workwith Valery Glizer)
Finite-horizon singular H∞ control problem: a regularization method
We consider an finite horizon singular H∞ control problem.
A regularization of this problem is proposed leading to a new
H∞ problem with a partial cheap control. The latter is solved
by adapting a perturbation technique. Then, it is shown on how
accurately the controller, solving this H∞ partial cheap control
problem, solves the original singular H∞ control problem.
Jianzhe (Trevor) Zhen, EPFL (joint work with Daniel Kuhn,
Wolfram Wiesemann)
Distributionally Robust Nonlinear Optimization
Leveraging a generalized ‘primal-worst equals dual-best’ duality scheme for robust optimization, we derive from first principles a strong duality result that relates distributionally robust
to classical robust optimization problems and that obviates theneed to mobilize the machinery of abstract semi-infinite duality
theory. In order to illustrate the modeling power of the proposed
approach, we present convex reformulations for data-driven
distributionally robust optimization problems whose ambiguity
sets constitute type-p Wasserstein balls for any p ∈ [1, ∞].
Frans de Ruiter, Tilburg University, The Netherlands (joint
work with Aharon Ben-Tal, Ernst Roos, Jianzhe (Trevor) Zhen,
Dick den Hertog)
Effective approach for hard uncertain convex inequalities
In many problems the uncertain parameters appear in aconvex way, which is problematic as no general techniques
exist for such problems. In this talk, we provide a systematic
way to construct safe approximations to such constraints when
the uncertainty set is polyhedral. We reformulate the original
problem as a linear adjustable robust optimization problem inwhich the nonlinearity of the original problem is captured by
the new uncertainty set. We demonstrate the quality of the approximations with a study of geometric programming problems
and numerical examples from radiotherapy optimization.

Parallel Sessions Abstracts Wed.3 16:00–17:15

Napat Rujeerapaiboon, National University of Singapore (joint
work with Cagil Kocyigit, Daniel Kuhn)
Robust Multidimensional Pricing: Separation without Regret
We study a robust monopoly pricing problem with a minimax
regret objective, where a seller endeavors to sell multiple goods
to a single buyer, only knowing that the buyer’s values for the
goods range over a rectangular uncertainty set. We interpret
this problem as a zero-sum game between the seller, whochooses a selling mechanism, and a fictitious adversary, whochooses the buyer’s values. We prove that this game admitsa Nash equilibrium that can be computed in closed form. We
further show that the deterministic restriction of the problem
is solved by a deterministic posted price mechanism.
Wolfram Wiesemann, Imperial College Business School (joint
work with Zhi Chen, Daniel Kuhn)
Data-Driven Chance Constrained Programs over Wasserstein Balls
We provide an exact deterministic reformulation for datadriven chance constrained programs over Wasserstein balls. For
individual chance constraints as well as joint chance constraints
with right-hand side uncertainty, our reformulation amounts
to a mixed-integer conic program. In the special case of a
Wasserstein ball with the 1-norm or the ∞-norm, the cone is
the nonnegative orthant, and the chance constrained program
can be reformulated as a mixed-integer linear program.
William Haskell, Purdue University (joint work with Hien Le
Thi Khanh, Renbo Zhao)
An Inexact Primal-Dual Smoothing Framework with Applications to Robust Optimization
We propose an inexact primal-dual smoothing framework tosolve a strongly-convex- generally-concave saddle point problem
with non-bilinear structure, with potentially a large number ofcomponent functions. We develop a probabilistic version of oursmoothing framework, which allows each sub-problem to besolved by randomized algorithms inexactly in expectation. In
addition, we extend both our deterministic and probabilistic
frameworks to solve generally convex-concave saddle point
problems. We comment on the application of this method torobust data-driven optimization.

SPA
Wed.3 H 2038
Block Alternating Schemes for Nonsmooth Optimization
at a Large Scale
Organizer: Emilie Chouzenoux
Chair: Jean-Christophe Pesquet
Krzysztof Rutkowski, Warsaw University of Technology (joint
work with Ewa Bednarczuk)
On projected dynamical system related to best approximation primal-dual algorithm for solving maximally monotone
operators inclusion problem
In this talk we discuss Projected Dynamical System (PDS)
associated with the proximal primal-dual algorithm for solving
maximally monotone inclusions problem. We investigate thediscretized version of (PDS) with the aim of giving an insight
into the relationships between the trajectories solving (PDS)
and sequences generated by proximal primal-dual algorithm.
We relate the discretization of (PDS) with Haugazeau-based
proximal primal-dual algorithms and discuss its block versions.
The talk is based on the common work with Ewa Bednarczuk.
Marco Prato, Università degli Studi di Modena e Reggio Emilia(joint work with Silvia Bonettini, Simone Rebegoldi)
A variable metric nonlinear Gauss-Seidel algorithm for nonconvex nonsmooth optimization
We propose a block coordinate forward-backward algorithm
aimed at minimizing functions defined on disjoint blocks ofvariables. The proposed approach is characterized by the possibility of performing several variable metric forward-backward
steps on each block of variables, in combination with an Armijo
backtracking linesearch to ensure the sufficient decrease of theobjective function. The forward-backward step may be computed approximately, according to an implementable stopping
criterion, and the parameters defining the variable metric may
be chosen using flexible desired adaptive rules.
Jean-Christophe Pesquet, University Paris Saclay (joint
work with Luis Briceño-Arias, Giovanni Chierchia, EmilieChouzenoux)
A Random Block-Coordinate Douglas-Rachford Splitting
Method with Low Computational Complexity for Binary
Logistic Regression
In this talk, we introduce a new optimization algorithm
for sparse logistic regression based on a stochastic version of
the Douglas Rachford splitting method. Our algorithm sweeps
the training set by randomly selecting a mini-batch of dataat each iteration, and it allows us to update the variables
in a block coordinate manner. Our approach leverages theproximity operator of the logistic loss, which is expressed withthe generalized Lambert W function. Experiments carried out
on standard datasets demonstrate the efficiency of our approachw.r.t. stochastic gradient-like methods.

Wed.3 16:00–17:15

ROB
Wed.3 H 3006
Algorithms and Applications of Robust Optimization
Organizer: William Haskell
Chair: William Haskell

141

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STO
Wed.3 H 3012
Stochastic Optimization and Its Applications (Part III)
Organizer: Fengmin Xu
Chair: Fengmin Xu
Christian Bayer, WIAS (joint work with Raul Tempone, Sören
Wolfers)
Pricing American options by exercise rate optimization
We consider the pricing American basket options in a multivariate setting, including the Heston and the rough Bergomi
models. In high dimensions, nonlinear PDEs methods for solving the problem become prohibitively costly due to the curse ofdimensionality. Our novel method uses Monte Carlo simulationand the optimization of exercise strategies parametrized as
randomized exercise strategies. Integrating analytically over
the random exercise decision yields an objective function thatis differentiable with respect to perturbations of the exercise
rate, even for finitely many sampled paths.
Xiaobo Li, National University of Singapore (joint work with
Saif Benjaafar, Daniel Jiang, Xiang Li)
Inventory Repositioning in On-Demand Product Rental
Networks
We consider a product rental network with a fixed numberof rental units distributed across multiple locations. Becauseof the randomness in demand and in the length of the rental
periods and in unit returns, there is a need to periodically
reposition inventory. We formulate the problem as a Markov
Decision Process and offer a characterization of the optimal
policy. In addition, we propose a new cutting-plane-based approximate dynamic programming algorithm that leverages the
structural properties. A novel convergence analysis, together
with promising numerical experiments, is provided.

Thu.1 09:00–10:15

Parallel Sessions Abstracts Thu.1 09:00–10:15

143

Thu.1 09:00–10:15

Okan Ozener, Ozyegin University (joint work with Basak Altan)
A Game Theoretical Approach for Improving the Operational Efficiencies of Less-than-truckload Carriers ThroughLoad Exchanges
Less-than-truckload (LTL) transportation offers fast, flexible
and relatively low cost transportation services to shippers. In
order to cope with the effects of economic recessions, LTL
industry implemented ideas such as reducing excess capacity
and increasing revenues through better yield management. In
this paper, we extend these initiatives beyond the reach ofindividual carriers and propose a collaborative framework thatfacilitates load exchanges to reduce the operational costs ofthe collaborating LTL carriers.
Mingyu Li, Department of Industrial Economics and Technology Management, NTNU (joint work with Peter Schütz)
Decision support for ship routing in dynamic and stochastic ice conditions
We discuss the problem of finding the optimal route for aship sailing in non-stationary ice conditions, with uncertainty
resulted from poor quality of ice conditions forecasts. We formulate the problem as a shortest path problem with uncertain
and time-dependent travel times but alternative objective canbe risk or cost. We consider a 15-day planning horizon, withnew ice information updated regularly and reconsider decisions
accordingly. We present the look-ahead model formulation, astochastic dynamic programming based solution method andpreliminary results.
Ali Ekici, Ozyegin University (joint work with Serhan Duran,Okan Ozener)
Ordering from Capacitated Suppliers under Cycle Time
Limitation
We study the ordering policy of a retailer which procures asingle item from multiple capacitated suppliers and satisfies
an exogenous demand. Retailer’s objective is to minimize total
fixed, variable and inventory holding cost. We develop cyclic
ordering policies for the retailer with limited cycle time. We
propose a novel iterative-natured heuristic framework whichlooks for the best cycle time while selecting suppliers andallocating orders. Computational experiments show that the
proposed heuristic framework provides better results comparedto other methods in the literature.

BIG
Thu.1 H 0105
Distributed and Decentralized Optimization: Learning,
Lower-Complexity Bounds, and Communication-Efficiency
Organizers: Andy Sun, Wotao Yin
Chair: Kaizhao Sun
Qing Ling, Sun Yat-Sen University
RSA: Byzantine-Robust Stochastic Aggregation Methods
for Distributed Learning from Heterogeneous Datasets
We propose a class of robust stochastic methods for distributed learning from heterogeneous datasets at presence ofan unknown number of Byzantine workers. The key is a regularization term incorporated with the cost function to robustify
the learning task. In contrast to most of the existing algorithms,
the resultant Byzantine-Robust Stochastic Aggregation (RSA)
methods do not rely on the assumption that the data are i.i.d.
on the workers. We show that the RSA methods converge toa near-optimal solution with the learning error dependent on
the number of Byzantine workers.
Mingyi Hong, University of Minnesota (joint work with Haoran
Sun)
Distributed Non-Convex First-Order Optimization and Information Processing: Lower Complexity Bounds and Rate
Optimal Algorithms
We consider a distributed non-convex optimization problems,
in which a number of agents collectively optimize a sum ofsmooth local objective functions. We address the question:
What is the fastest rate that any distributed algorithms canachieve, and how to achieve those rates. We develop a lower
bound analysis, and show that p
in the worst-case it takes any
first-order algorithm O(1/( ∗ (k))) iterations to achievesome -solution, where k is the network spectrum gap. We
also develop a rate-optimal distributed method whose rate
matches the lower bound. The algorithm combines ideas from
distributed consensus, as well as classical fast solvers for linear
systems.
Wotao Yin, University of California, Los Angeles
LAG: Lazily Aggregated Gradient is CommunicationEfficient in Distributed and Decentralized Optimization
In Lazily Aggregated Gradient (LAG), the nodes in a distributed or decentralized algorithm can choose to use outdated
gradient information, therefore, reduce communication. This isalso called gradient censoring. Surprisingly, the original convergence rates remain the same in the strongly-convex, convex,
and nonconvex cases while communication is reduced. When
local Lipschitz constants are heterogeneous, communicationis reduced significantly. Numerical experiments with synthetic
and real data will demonstrate this significant communication
reduction. This talk combines different recent works.

Thu.1 09:00–10:15

APP
Thu.1 H 3004
Applications in Logistics and Supply Chain Management
Chair: Ali Ekici

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BIG
Thu.1 H 3006
Large-Scale Optimization for Statistical Learning
Organizer: Paul Grigas
Chair: Paul Grigas

BIG
Thu.1 H 3007
Big Data and Machine Learning - Contributed Session I
Chair: Guozhi Dong

Rahul Mazumder, Massachusetts Institute of Technology
Algorithmic approaches for Nonparametric Function estimation with Shape Constraints [canceled]

Scindhiya Laxmi, Indian institute of Technology Roorkee (joint
work with S. K. Gupta)
Human Activity Recognition using Intuitionistic Fuzzy
Proximal Support Vector Machines

Nonparametric function estimation is a quintessential problem in statistical data-analysis with widespread applications in
online advertising, econometrics, operations research, etc. We
consider the problem of estimating functions are (a) smoothand have (b) shape constraints (monotonicity, convexity, etc.).
This leads to an infinite-dimensional convex quadratic optimization problem posing computational challenges. We propose aframework to jointly achieve goals (a), (b). Time permitting,
we will discuss the problem of fitting a multivariate convex
function to data.

Human activity recognition is an active area of research in
Computer Vision. One of the difficulties of an activity recognition system is the presence of noises in the training data. We
addressed this problem by assigning membership and hesitation
degrees to the training instances according to its contribution
to the classification problem. In this way, the impact of noises
and outliers has been reduced to some extent. The methodology intuitionistic fuzzy proximal support vector machine forhuman activity is proposed to speed up the training phase andto increase the classification accuracy.

Hussein Hazimeh, MIT (joint work with Rahul Mazumder)Learning Hierarchical Interactions at Scale [canceled]

Robert Ravier, Duke University (joint work with Vahid Tarokh)
Online Optimization for Time Series of Parametrizable Objective Functions

In many learning settings, it is beneficial to augment themain features with pairwise interactions. Such interaction models can be enhanced by performing variable selection under
the so-called “strong hierarchy” constraint. Existing convex
optimization based algorithms face difficulties in handling problems where the number of main features is beyond a thousand.
We propose convex first order algorithms to exactly solve theproblem with up to a billion variables on a single machine. We
also discuss how to solve the original nonconvex problem via atailored branch-and-bound algorithm.

The time-varying component of many objective functions ofpractical interest in online optimization is fully contained in a
finite number of parameters. This allows us to use methods
from standard time series analysis in order to predictively optimize. We investigate the theoretical and computational effects
of using these methods. In particular, we focus on both thepotential improvements of regret upper bounds when utilizing these techniques as well as algorithms for simultaneously
modeling and optimizing.
Guozhi Dong, Humboldt-Universität zu Berlin (joint work withMichael Hintermüller, Kostas Papafitsoros)
A data-driven model-based method for quantitative MRI
Recently, an integrated physics-based model has been introduced for quantitative magnetic resonance imaging. It incorporates the physical model by Bloch equations into the dataacquisition, and leads to high accuracy estimation of the tissue
parameters. However, in general, the physical model might
be not explicitly known. In this talk, we introduce a novel
data-driven model-based method which combines the advancesof integrated physics-based method with deep neural networks.

Thu.1 09:00–10:15

Parallel Sessions Abstracts Thu.1 09:00–10:15

145

VIS
Thu.1 H 3013
Complementarity: Extending Modeling and Solution Techniques (Part II)
Organizer: Michael Ferris
Chair: Michael FerrisCON
Thu.1 H 2032
Methods and Applications in Mixed Integer Semidefinite
Programming
Organizer: Christoph Helmberg
Chair: Christoph Helmberg

Francesco Caruso, University of Naples ”Federico II” (joint
work with Maria Carmela Ceparano, Jacqueline Morgan)
Approximation of Nash equilibria via continuous optimization

Angelika Wiegele, Alpen-Adria-Universität Klagenfurt (joint
work with Nicolo Gusmeroli, Franz Rendl)
An Exact Penalty Method over Discrete Sets

David Stewart, University of Iowa (joint work with MarioBarela)Simulating electrical circuits with diodes and transistors
Electrical circuits with dynamical elements (inductors andcapacitors) are implicit differential algebraic equations, andwhen ideal diodes are included they are differential complementarity problems. The matrices that arise in these problems are
symmetric positive definite, which means solutions exist andare unique. These systems can be efficiently solved numericallyby high order Diagonally Implicit Runge-Kutta methods in a
way that exploits the network structure.

We are interested in solving linearly constrained binary
quadratic problems (BQP) by transforming the problems into
unconstrained ones and using a max-cut solver. This is in thespirit of “exact penalty methods”, but optimizing the penalized
function over the discrete set. We improve on a method investigated by Lasserre (2016) who computed a sufficiently large
penalty paramter by solving two semidefinite programs. Our
new parameters lead to a better performance when solving thetransformed problem. We present preliminary computational
results demonstrating the strength of this method.
Julie Sliwak, RTE, Polytechnique Montréal, LIPN (joint workwith Miguel Anjos, Lucas Létocart, Jean Maeght, Emiliano
Traversi)
A conic-bundle approach to solve semidefinite relaxationsof Alternating Current Optimal Power Flow problems
The Alternating Current Optimal Power Flow (ACOPF) problem is a challenging problem due to its significant nonconvexity.
To prove global optimality, Semidefinite Relaxations (SDRs)
are a powerful tool: the rank-one SDR is often exact and when
it is not, the Lasserre hierarchy SDRs achieve global optimality.
However, solving large-scale semidefinite problems is still acomputational challenge. We propose a conic-bundle algorithm
to solve SDRs of ACOPF based on a clique decomposition
arising from an ad-hoc chordal extension of the power network.
Felix Lieder, Heinrich-Heine-Universität Düsseldorf
Exploiting Completely Positive Projection Heuristics
In this talk we revisit completely positive cone programs. A
simple reformulation as a fixed point problem leads us to a
family of (globally convergent) outer algorithms. NP-hardnessof the original problem is shifted to the completely positive
projection operator. Its sequential (approximate) evaluations
are then tackled by exploiting the underlying structure in combination with powerful local optimization tools. Finally somepromising numerical results on small dimensional maximum
stable set problems are presented, indicating that the resulting
heuristics often perform well in practice.

Thu.1 09:00–10:15

We propose an algorithm based on a non-standard (nonconvex) relaxation of the classical best response approach whichguarantees the convergence to a Nash equilibrium in two-player
non zero-sum games when the best response functions are notlinear, both their compositions are not contractions and thestrategy sets are Hilbert spaces. First, we prove a uniquenessresult for Nash equilibria. Then, we define an iterative method
and a numerical approximation scheme, relying on a continuous
optimization technique, which converge to the unique Nashequilibrium. Finally, we provide error bounds.

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CON
Thu.1 H 2033
Matrix Analysis and Conic Programming
Organizer: Etienne de Klerk
Chair: Fei Wang
Cécile Hautecoeur, UCLouvain (joint work with François
Glineur)
Nonnegative matrix factorization over polynomial signals
via a sum-of-squares approach
Nonnegative matrix factorization (NMF) is a linear dimensionality reduction technique successfully used for analysis ofdiscrete data, such as images. It is a discrete process by nature
while the analyzed signals may be continuous. The aim of thiswork is to extend NMF to deal with continuous signals, avoiding
a priori discretization. In other words, we seek a small basisof nonnegative signals capable of additively reconstructing alarge dataset. We propose a new method to compute NMF
for polynomials or splines using an alternating minimization
scheme relying on sum-of-squares representation.
Fei Wang, Royal Institute of Technology (KTH)
Noisy Euclidean Distance Matrix Completion with A Single Missing Node
We present several solution techniques for the noisy single
source localization problem,i.e., the Euclidean distance matrix
completion problem with a single missing node to locate undernoisy data. For the case that the sensor locations are fixed, weshow that this problem is implicitly convex, and we provide apurification algorithm along with the SDP relaxation to solve it
efficiently and accurately. For the case that the sensor locations
are relaxed, we study a model based on facial reduction. We
present several approaches to solve this problem efficiently andcompare their performance.

CON
Thu.1 H 2038
Second and Higher Order Cone Programming and Trust
Region Methods
Organizer: Etienne de Klerk
Chair: Walter Gomez
Mirai Tanaka, The Institute of Statistical Mathematics (joint
work with Takayuki Okuno)
An adaptive LP-Newton method for second-order cone optimization
The LP-Newton method solves the linear optimization problem (LP) by repeatedly projecting a current point onto a certain relevant polytope. In this talk, we extend the LP-Newton
method to the second-order cone optimization problem (SOCP)
via a linear semi-infinite optimization problem (LSIP) reformulation. In the extension, we produce a sequence by projection
onto polyhedral cones constructed from LPs obtained by finitely
relaxing the LSIP. We show the global convergence of the
proposed algorithm under mild assumptions, and investigate
investigate its efficiency through numerical experiments.
Walter Gomez, Universidad de La Frontera (joint work with
Nicolas Fuentealba)
Exact relaxations of non convex trust region subproblems
with an additional quadratic constraint.
In this work we study nonconvex trust region problems withan additional quadratic constraint. For this class of problems
we propose a family of relaxations, and study some key properties regarding convexity, exactness of the relaxation, sufficientcondition for exactness, etc. For the case that the additional
quadratic constraint is not convex , we generalize sufficientconditions for exactness of the relaxation proposed originallyfor trust region problems with an additional conic and discuss
some examples for which trivial extensions of the sufficientconditions do not apply.
Lijun Ding, Cornell University (joint work with Lek-Heng Lim)
Higher-Order Cone Programming [canceled]Thu.1 09:00–10:15

We introduce a conic embedding condition that gives a hierarchy of cones and cone programs. This condition is satisfied
by a large number of convex cones including the cone of copositive matrices, the cone of completely positive matrices, andall symmetric cones. We discuss properties of the intermediate cones and conic programs in the hierarchy. In particular,
we demonstrate how this embedding condition gives rise to a
family of cone programs that interpolates between LP, SOCP,
and SDP.

Parallel Sessions Abstracts Thu.1 09:00–10:15

Francisco Javier Aragón Artacho, University of Alicante (joint
work with Yair Censor, Aviv Gibali)
The cyclic Douglas–Rachford algorithm with r-setsDouglas–Rachford operators
The Douglas–Rachford (DR) algorithm is an iterative procedure that uses sequential reflections onto convex sets andwhich has become popular for convex feasibility problems. In
this paper we propose a structural generalization that allows
to use r-sets-DR operators in a cyclic fashion. We prove convergence and present numerical illustrations of the potential
advantage of such operators with r > 2 over the classical
2-sets-DR operators in a cyclic algorithm.
Sorin-Mihai Grad, Vienna University (joint work with Oleg Wilfer)
Combining duality and splitting proximal point methods
We approach via conjugate duality some optimization problems with intricate structure that cannot be directly solved bymeans of the existing proximal point type methods. A splitting scheme is employed on the dual problem and the optimal
solutions of the original one are recovered by means of optimality conditions. We use this approach for minmax locationand entropy constrained optimization problems, presenting also
some computational results where our method is comparedwith some recent ones from the literature.
Xiaoqun Zhang, Shanghai Jiao Tong University
A Stochastic Primal Dual Fixed Point Method for Composited Convex Optimization
In this work we proposed a stochastic version of primaldual fixed point method(SPDFP) for solving a sum of two
proper lower semi-continuous convex function one of which iscomposited with a linear operator. Under some assumptions
we proved the convergence of the proposed algorithm andalso provided a variant for the implementation. Convergence
analysis shows that the expected error of iterate is of the orderO(k−α ) ) where k is the iteration number and α ∈ (0, 1).
Finally, two numerical examples are performed to demonstrate
the effectiveness of the proposed algorithms.

DER
Thu.1 H 3025
Emerging Trends in Derivative-Free Optimization(Part III)
Organizers: Ana Luisa Custodio, Sébastien Le Digabel,
Margherita Porcelli, Francesco Rinaldi, Stefan Wild
Chair: Ana Luisa Custodio
Susan Hunter, Purdue University (joint work with Kyle Cooper,
Kalyani Nagaraj)
Bi-objective simulation optimization on integer lattices using the epsilon-constraint method in a retrospective approximation framework
We propose the Retrospective Partitioned Epsilon-constraint
with Relaxed Local Enumeration (R-PERLE) algorithm to solve
the bi-objective simulation optimization problem on integer
lattices. R-PERLE is designed for simulation efficiency andprovably converges to a local efficient set w.p.1 under appropriate regularity conditions. It uses a retrospective approximation(RA) framework and solves each resulting bi-objective samplepath problem only to an error tolerance commensurate withthe sampling error. We discuss the design principles that makeour algorithm efficient.
Mauro Passacantando, Università di Pisa (joint work with Stefano Lucidi, Francesco Rinaldi)
A global optimization approach for solving non-monotone
equilibrium problems
A global optimization approach for solving non-monotone
equilibrium problems (EPs) is proposed. The regularized gap
functions are used to reformulate any EP as a constrained
global optimization program and some bounds on the Lipschitz constant of such functions are provided. The proposed
approach combines exact penalty functions with an improved
version of the DIRECT algorithm which exploits local boundsof the Lipschitz constant. Unlike most existing methods, nomonotonicity condition is assumed. Preliminary numerical results on several classes of EPs show the effectiveness of theapproach.
Evgeniya Vorontsova, Université Grenoble Alpes (joint workwith Pavel Dvurechensky, Alexander Gasnikov, Eduard Gorbunov)[moved] Optimal stochastic gradient-free methods with
inexact oracle
We propose new accelerated stochastic gradient-free methods for convex and strongly convex optimization problems withsmall additive noise of unknown nature. We try to make theanalysis accurate to obtain the best possible estimates on how
this noise accumulates. Note that we don’t assume that we“live” in a bounded set. Since that we have to show that generated sequence of points will be bounded. This requires newtechnique.

Thu.1 09:00–10:15

CNV
Thu.1 H 1028
Splitting Methods and Applications (Part I)
Organizers: Pontus Giselsson, Ernest Ryu, Adrien Taylor
Chair: Adrien Taylor

147

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GLO
Thu.1 H 1029
Global Optimization - Contributed Session II
Chair: Ozan Evkaya
Majid Darehmiraki, Behbahan Khatam Alanbia University ofTechnology (joint work with Seyedeh Masoumeh Hosseini Nejad)
The proximal gradient method for the subproblem of trust
region method
Trust region algorithms are a class of numerical methods fornonlinear optimization problems, which have been extensively
studied for many decades. Finding solution for the subproblem of the trust region method is a fundamental problem innonlinear optimization. In this paper, using Tikhonov regularization, we first turn the constrained quadratic problem into
an unconstrained problem, and then solve it by the proximal
gradient (PG) method. The Proposed method is effective bothfor dense and large-sparse problems, including the so-called
hard case.
José Fernández, University of Murcia (joint work with Laura
Anton-Sanchez, Boglárka G.-Tóth, Juana L. Redondo, Pilar
M. Ortigosa)
Firm expansion: how to invest? A MINLP model and B&B
and heuristic procedures to deal with it
A chain wants to expand its presence in a given region, where
it already owns some existing facilities. The chain can open a
new facility and/or modify the qualities of some of its existing
facilities and/or close some of them. In order to decide thelocation and quality of the new facility (in case it is open) as
well as the new qualities for the existing facilities, a MINLP is
formulated, whose objective is to maximize the profit obtainedby the chain. Both an exact interval Branch-and-Bound method
and a heuristic evolutionary algorithm are proposed to copewith this problem.
Ozan Evkaya, Atilim University
Parameter estimations of finite mixture models with particular optimization tools

Thu.1 09:00–10:15

Copulas can be easily incorporated into finite mixtures but
as the structure of mixture models the gradient based optimization tools are not practical for the purpose of parameter
estimation. This paper mainly investigates the performance ofvarious optimization tools with different properties in terms offinding parameters of finite mixture models. In that respect,traditional gradient based, recently developed heuristic andglobal optimization algorithms are studied. For testing performance, considered problem with specific optimization routines
are compared in terms of accuracy and run time

MUL
Thu.1 H 0111
Uncertainty in Multi-Objective Optimization
Organizers: Gabriele Eichfelder, Alexandra Schwartz
Chair: Patrick Groetzner
Fabian Chlumsky-Harttmann, TU Kaiserslautern (joint workwith Anita Schöbel)
Cutting planes for robust multi-objective optimization
Previously developed concepts for robust multi-objective
optimization such as set-based and point-based minmax robust
efficiency determine efficient solutions that are good in theworst case. However, finding robust efficient solutions for agiven uncertain multi-objective problem remains difficult. We
adopt a row generating approach incrementally increasing theset of considered scenarios and investigate what selection rule
for the scenario to add is most beneficiary to achieve fast
convergence.
Patrick Groetzner, Augsburg University (joint work with Ralf
Werner)Multiobjective Optimization Under Uncertainty: A Robust
Regret Approach
Consider a multiobjective decision problem with uncertainty
given as a set of scenarios. In the single criteria case, robust
optimization methodology helps to identify solutions which
remain feasible and of good quality for all possible scenarios.
Here one method is to compare the possible decisions under
uncertainty against the optimal decision with the benefit ofhindsight, i.e. to minimize the regret of not having chosen
the optimal decision. In this talk I will extend this regret tothe multiobjective setting to introduce a robust strategy for
multiobjective optimization under uncertainty.

Parallel Sessions Abstracts Thu.1 09:00–10:15

Haihao Lu, MIT
Q-Stochastic Gradient Descent: a New Stochastic FirstOrder Method for Ordered Empirical Loss Minimization
We propose a new approach to stochastic first-order method
for tackling empirical loss minimization problems such as thosethat arise in machine learning. We develop a theory and computationally simple way to construct a gradient estimate thatis purposely biased. On the theory side, we show that the
proposed algorithm is guaranteed to converges at a sublinear
rate to the global optimum of the modified objective function.
On the computational side, we present numerical experimentsthat confirm the usefulness of the new method compared withstandard SGD in different settings.
Tianbao Yang, University of Iowa (joint work with Rong Jin,
Qihang Lin, Qi Qi, Yi Xu)
Stochastic Optimization for DC Functions and Nonsmooth Non-convex Regularizers with Non-asymptotic
Convergence
We propose new stochastic optimization algorithms andstudy their first-order convergence theories for solving a broadfamily of DC functions. We improve the existing algorithms andtheories of stochastic optimization for DC functions from bothpractical and theoretical perspectives. Moreover, we extend
the proposed stochastic algorithms for DC functions to solve
problems with a general non-convex non-differentiable regularizer, which does not necessarily have a DC decomposition but
enjoys an efficient proximal mapping.
N. Serhat Aybat, Penn State University (joint work with Afrooz
Jalilzadeh, Uday Shanbhag, Erfan Y. Hamedani)
A Doubly-Randomized Block-Coordinate Primal-Dual
Method for Large-scale Convex-Concave Saddle Point
Problems: Acceleration via Variance-Reduction
We consider computing a saddle point (x∗ , y ∗ ) of convexPNconcave L(x, y) =Φ (x, y). This problem class inn=1 ncludes convex optimization with a large number of constraints.
To contend with the challenges in computing full gradients,
we employ a primal-dual scheme in which randomly selectedprimal and dual variable blocks, xi and yj , are updated atevery iteration. When N is large, we can utilize an increasing
batch-size of the gradients of Φn , to achieve the optimal rate ofO(1/k) in terms of L(xk , y ∗ ) − L(x∗ , y k ). Preliminary results
on QCQPs with many constraints look promising.

NON
Thu.1 H 0106
Optimality Conditions in Nonlinear Optimization (Part I)
Organizer: Gabriel Haeser
Chair: Paulo J. S. Silva
Alexey Tret’yakov, University of Podlasie in Siedlce (joint workwith Olga Brezhneva, Yuri Evtushenko)
Elementary proofs of a Karush-Kuhn-Tucker Theorem
We present elementary proofs of a Karush-Kuhn-Tucker(KKT) theorem for an optimization problem with inequality
constraints. We call the proofs “elementary” because they
do not use advanced results of analysis. Our proofs of the
KKT theorem use only basic results from Linear Algebra and adefinition of differentiability. The simplicity of the proofs makes
them particularly suitable for use in a first undergraduate course
in optimization.
Bruno Figueira Lourenço, University of Tokyo
Amenable cones, error bounds and algorithms
We will talk about a new family of cones called “amenable
cones”. We will show that feasibility problems over amenable
cones admit error bounds even when constraint qualifications
fail to hold. Therefore, amenable cones provide a natural framework for extending the so-called ”Sturm’s Error Bound”. We
will present many example of amenable cones and discuss theconnection between the amenability condition and other properties such as niceness and facial exposedness. At the end, we
will also discuss algorithmic implications of our results.
Paulo J. S. Silva, University of Campinas - UNICAMP (joint
work with Roberto Andreani, Gabriel Haeser, Leornardo D.
Secchin)Sequential optimality conditions for mathematical problems with complementarity constraints
Recently, the convergence of methods for nonlinear optimization problems has been analyzed using sequential optimality
conditions. However, such conditions are not well suited forMathematical Problems with Complementarity Constraints(MPCCs). Here we propose new sequential conditions forusual stationarity concepts in MPCC, namely weak, C- andM-stationarity, together with the associated weakest MPCC
constraint qualifications. We also show that some algorithms
reach AC-stationary points, extending their convergence results.
The new results include the linear case, not previously covered.
Thu.1 09:00–10:15

NON
Thu.1 H 0104
Recent Advances of First-Order Methods for Nonlinear
Problems (Part I)
Organizers: Shiqian Ma, Yangyang Xu
Chair: Shiqian Ma

149

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NON
Thu.1 H 0107
Methods of Optimization in Riemannian Manifolds (Part I)
Organizer: Orizon Pereira Ferreira
Chair: Orizon Pereira FerreiraJoão Xavier da Cruz Neto, Federal University of Piauı́ (joint
work with Lucas Meireles, Gladyston de Carvalho Bento)
An Inexact Proximal Point Method for Multiobjective Optimization on Riemannian Manifolds
In this talk, is introduced a definition of approximate Pareto
efficient solution as well as a necessary condition for such
solutions in the multiobjective setting on Riemannian manifolds. It is also propose an inexact proximal point method fornonsmooth multiobjective optimization by using the notion of
approximate solution. The main convergence result ensuresthat each cluster point (if any) of any sequence generated by
the method is a Pareto critical point. Furthermore, when the
problem is convex on a Hadamard manifold, full convergenceof the method for a weak Pareto optimal is obtained.
João Carlos Souza, Federal University of Piauı́ (joint work with
Yldenilson Almeida, Paulo Oliveira, João Xavier da Cruz Neto)
Accelerated proximal-type method for DC functions in
Hadamard manifolds
In this work, we consider a proximal-type method for solving
minimization problems involving smooth DC (difference of convex) functions in Hadamard manifolds. The method accelerates
the convergence of the classical proximal point method for DC
functions and, in particular, convex functions. We prove thatthe point which solves the proximal subproblem can be used
to define a descent direction of the objective function. Our
method uses such a direction together with a line search forfinding critical points of the objective function. We illustrate
our results with some numerical experiments.
Maurı́cio Louzeiro, UFG (joint work with Orizon Pereira Ferreira, Leandro Prudente)
Gradient Method for Optimization on Riemannian Manifolds with Lower Bound CurvatureThu.1 09:00–10:15

The gradient method for minimize a differentiable convex
function on Riemannian manifolds with lower bounded sectional
curvature is analyzed in this talk. The analysis of the method ispresented with three different finite procedures for determining
the step size, namely, Lipschitz stepsize, adaptive stepsize andArmijo’s stepsize. Convergence of the whole sequence to aminimizer, without any level set boundedness assumption, is
proved. Iteration-complexity bound is also presented. As wellas, some examples of functions that satisfy the assumptions of
our results.

NON
Thu.1 H 2053
Mixed-Integer Optimal Control and PDE Constrained Optimization (Part III)
Organizers: Falk Hante, Christian Kirches, Sven Leyffer
Chair: Christian Kirches
Clemens Zeile, Otto-von-Guericke University Magdeburg (joint
work with Francesco Braghin, Nicolò Robuschi, Sebastian
Sager)
Multiphase Mixed-Integer Nonlinear Optimal Control ofHybrid Electric Vehicles
This talk considers the problem of computing the minimumfuel energy management strategy of a hybrid electric vehicle
on a given driving cycle. A tailored algorithm is devised byextending the combinatorial integral approximation techniquethat breaks down the original mixed-integer nonlinear program
into a sequence of nonlinear programs and mixed-integer linear
programs. Specifically, we address the multiphase context andpropose combinatorial constraints for the optimal gear choice,torque split and engine on/off controls in order to account fortechnical requirements.
Bart van Bloemen Waanders, Sandia National Laboratory
Mixed Integer PDE-constrained optimization with scalable
softwareWe present algorithms to solve mixed integer problems constrained by partial differential equations (PDEs). A branch andbound methodology is used to address the discrete nature of
the problem and at each bound computation the relaxation
requires the solution of a PDE-constrained optimization problem. A hierarchical modeling approach (with multiple starts)
efficiently supports the bounding process. Low-fidelity models
allow exploration for the most promising branching options.
We discuss our C++ software design which features scalable
parallelism. A numerical prototype demonstrates our approach.
Sven Leyffer, Argonne National Laboratory (joint work withTodd Munson, Ryan Vogt)
Mixed-Integer PDE Constrained Optimization for Designof Electromagnetic Cloaking
We formulate a mixed-integer partial-differential equation
constrained optimization problems (MIPDECO) for designing an electromagnetic cloak governed by the 2D Helmholtz
equation with absorbing boundary conditions. We extend theformulation to include uncertainty with respect to the angle ofthe incidence wave, and develop a mixed-integer trust-region
approach for solving both the nominal and the uncertain instance. Our method alternates between a forward/adjoint PDE
solve and a knapsack problem. We present numerical resultsof our new approach that demonstrate its effectiveness.

Parallel Sessions Abstracts Thu.1 09:00–10:15

151

PDE
Thu.1 H 0112
Geometric Methods in Optimization of Variational Problems (Part I)
Organizers: Roland Herzog, Anton Schiela
Chair: Roland Herzog

PDE
Thu.1 H 2013
PDE-constrained Optimization Under Uncertainty (Part I)
Organizers: Harbir Antil, Drew Philip Kouri, Thomas Michael
Surowiec, Michael Ulbrich, Stefan Ulbrich
Chair: Thomas Michael Surowiec

Ronny Bergmann, TU Chemnitz (joint work with Roland Herzog, Daniel Tenbrinck, Jose Vidal-Nunez)
A primal-dual algorithm for convex nonsmooth optimization on Riemannian manifolds

René Henrion, Weierstrass Institute for Applied Analysis andStochastics (WIAS) (joint work with Mohamad Hassan Farshbaf Shaker, Martin Gugat, Holger Heitsch)
Optimal Neumann boundary control of the vibrating stringwith uncertain initial data

Robin Richter, Georg-August Universität Göttingen (joint workwith Duy Hoang Thai, Stephan Huckemann)
Bilevel optimization in imaging with application to fingerprint analysis
Removing texture from an image while preserving sharpedges, to obtain a so-called cartoon, is a challenging task inimage processing, especially since the term texture is mathematically not well defined. We propose a generalized familyof algorithms, guided by two optimization problems simultaneously, profiting from the advantages of both variational calculus
and harmonic analysis. In particular, we aim at avoiding introduction of artefacts and staircasing, while keeping the contrastof the image.
Dominik Stöger, TU München, Fakultät für Mathematik (joint
work with Felix Krahmer)
The convex geometry of matrix completion
Matrix completion refers to the problem of reconstructing a
low-rank matrix from only a random subset of its entries. It
has been studied intensively in recent years due to various applications in learning and data science. A benchmark algorithm
for this problem is nuclear norm minimization, a natural convex
proxy for the rank. We will discuss the stability of this approach
under the assumption that the observations are perturbed byadversarial noise.

The talk addresses a PDE-constrained optimization problem,
namely the optimal control of vibrating string under probabilistic constraints. The initial position of the string is supposed
to be random (multiplicative noise on Fourier coefficients).
The objective is to find a cost optimal control driving the final
energy of the string to an amount smaller then epsilon withprobability larger than p. The numerical solution of the resulting convex optimization problem relies on the ‘spheric-radial
decomposition’ of Gaussian random vectors.
Harbir Antil, George Mason University
Risk-Averse Control of Fractional Diffusion
In this talk, we introduce and analyze a new class of optimal control problems constrained by elliptic equations withuncertain fractional exponents. We utilize risk measures toformulate the resulting optimization problem. We develop afunctional analytic framework, study the existence of solution
and rigorously derive the first-order optimality conditions. Additionally, we employ a sample-based approximation for theuncertain exponent and the finite element method to discretizein space.
Philipp Guth, University of Mannheim (joint work with Vesa
Kaarnioja, Frances Kuo, Claudia Schillings, Ian Sloan)
PDE-constrained optimization under uncertainty using aquasi-Monte Carlo method
In this work we apply a quasi-Monte Carlo (QMC) method
to an optimal control problem constrained by an elliptic PDE
equipped with an uncertain diffusion coefficient. In particular
the optimization problem is to minimize the expected valueof a tracking type cost functional with an additional penalty
on the control. It is shown that the discretization error of thesolution is bounded by the discretization error of the adjointstate. For the convergence analysis the latter is decomposed
into truncation error, finite element error and QMC error, which
are then analysed separately.

Thu.1 09:00–10:15

Based on a Fenchel dual notion on Riemannian manifolds weinvestigate the saddle point problem related to a nonsmoothconvex optimization problem. We derive a primal-dual hybrid
gradient algorithm to compute the saddle point using either
an exact or a linearized approach for the involved nonlinear
operator. We investigate a sufficient condition for convergenceof the linearized algorithm on Hadamard manifolds. Numericalexamples illustrate, that on Hadamard manifolds we are on parwith state of the art algorithms and on general manifolds weoutperform existing approaches.

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ROB
Thu.1 H 1058
Robust Optimization: New Advances in Methodology andApplications
Organizer: Nikos Trichakis
Chair: Nikos Trichakis

SPA
Thu.1 H 1012
Sparse Optimization and Information Processing - Contributed Session II
Organizer: Shoham Sabach
Chair: Mustafa C. Pinar

Lukáš Adam, Southern University of Science and Technology
Distributionally robust multi-objective optimization [canceled]Radu-Alexandru Dragomir, Université Toulouse Capitole & D.I.
Ecole Normale Supérieure
Fast Gradient Methods for Symmetric Nonnegative Matrix
Factorization

Recently, there were multiple papers concerning distributionally robust optimization. They usually consider a single
objective. In this talk, we generalize the concept into optimization problems with multiple objectives. We present several
approaches based on scalarization, Pareto front and set comparison. We show the advantages and disadvantages of all
approaches both from a theoretical point and computational
complexity. We conclude the talk by a numerical comparison.
Phebe Vayanos, University of Southern California (joint workwith John P. Dickerson, Duncan McElfresh, Eric Rice)
Robust Active Preference Learning
We consider the problem faced by a recommender systemthat seeks to offer a user their favorite item. Before making a
recommendation, the system has the opportunity to elicit theuser’s preferences by making a moderate number of queries.
We propose an exact robust optimization formulation of the
problem which integrates the learning (preference elicitation)and recommendation phases and an equivalent reformulation asa mixed-binary linear program. We evaluate the performance of
our approach on synthetic and real data from the US homelessyouth where we learn the preferences of policy-makers.
Nikos Trichakis, MIT (joint work with David Simchi-Levi, Peter
Yun Zhang)
Robust Optimization for Response Supply Chain Design
Against Bioattacks

Thu.1 09:00–10:15

We study end-to-end design of a supply chain for antibiotics to defend against bioattacks. We model the defender’sinventory prepositioning and dispensing capacity installation
decisions, attacker’s move, and defender’s adjustable shipment
decisions, so as to minimize inventory and life loss costs, subject to population survivability targets. We provide theoreticalbacking to the performance of the Affinely Adjustable Robust
Counterpart by proving its optimality under certain conditions.
We conduct a high-fidelity case study with millions of nodes
to guard against anthrax attacks in the US.

We describe fast gradient methods for solving the symmetric nonnegative matrix factorization problem (SymNMF). We
use recent results on non-Euclidean gradient methods andshow that the SymNMF problem is smooth relatively to awell-chosen Bregman divergence. This approach provides asimple hyper-parameter-free method which comes with theoretical convergence guarantees. We also discuss accelerated
variants. Numerical experiments on clustering problems showthat our algorithm scales well and reaches both state of theart convergence speed and clustering accuracy for SymNMF
methods.
Yue Xie, University of Wisconsin-Madison (joint work with
Uday Shanbhag)
A Tractable ADMM Scheme for Computing KKT Points
and Local Minimizers for l0 Minimization Problems
We consider an equivalent formulation of l0 -minimization
problem over a polyhedral set as MPCC. It is known that `0 norm penalty implicitly emphasizes sparsity of the solution. We
show that under suitable assumptions, an equivalence is derived
between KKT points and local minimizers of this formulation.
A perturbed variant of proximal ADMM is proposed for which
each subproblem is tractable and convergence can be claimed
under mild assumptions. Preliminary numerics suggest that thisscheme may significantly outperform their standard nonconvex
ADMM counterparts.
Mustafa C. Pinar, Bilkent University (joint work with Deniz
Akkaya)Minimizers in Robust Regression Adjusted for Sparsity
Robust regression analysis using Huber’s linear-quadratic
loss function has been studied in the context of numerical
optimization since the 1980s. Its statistical properties underdeviations from normality are well-known. We couple the Huber
loss function with a L0 -norm term to induce sparsity, and study
the local and global minimizers of the resulting non-convex
function inspired from results of Nikolova on the least squares
regression adjusted for sparsity.

Parallel Sessions Abstracts Thu.1 09:00–10:15

153

STO
Thu.1 H 3012
Advances in Probabilistic/Chance Constrained Problems
Chair: Pedro Pérez-Aros
Yassine Laguel, Université Grenoble Alpes (joint work with
Wellington De Oliveira, Jerome Malick, Guilherme Ramalho,
Wim Van Ackooij)
On the interplay between generalized concavity andchance constraints
Probabilistic constraints (or chance constraints) aim at providing safety regions for optimization problems exposed to
uncertainty. Understanding when these regions are convex isimportant for numerical resolution of the optimization problem.
In this work we show that “eventual convexity” often occurs :for structured but general models of chance constraints, convexity of levels sets is guaranteed when the probability level isgreater than a computable threshold. We illustrate our results
on examples uncovered by previous theory.
Pedro Pérez-Aros, Universidad de O’Higgins (joint work with
René Henrion, Wim Van Ackooij)
Generalized gradients for probabilistic/robust (probust)
constraints

Thu.2 10:45–12:00

Ideas from robust optimization lead to probust functions,i.e. probability functions acting on generalized semi-infinite
inequality systems. In this work, we employ powerful tools fromvariational analysis to study generalized differentiation of suchprobust functions. We also provide explicit outer estimates of
the generalized subdifferentials in terms of nominal data.

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Thu.2 10:45–12:00
APP

APP

Thu.2 H 3004
Applications in Economics
Chair: Po-An Chen

Thu.2 H 3012
Applications in Engineering
Chair: Manuel Radons

Benteng Zou, University of Luxembourg (joint work with
Luisito Bertinelli, Stephane Poncin)
The Dynamics Competition of Key Elements of Sustainable Technology – the Rare Earth Elements

Dimitrios Nerantzis, Imperial College London (joint work with
Filippo Pecci, Ivan Stoianov)Pressure and energy optimization of water distribution systems without storage capacity.

Rare earth elements are widely uses of i-phones, computers,
hybrid cars, wind and solar energy and are essentially important for the worldwide sustainable development. Few has been
done related to rare earth elements competition in economic
research. This current study tries to fill in this gap. A monopoly
to duopoly competition is constructed and studied. We provide
necessary and sufficient conditions when should enter the competition and what would be the consequences and responseof China. We also provide the analysis if cooperation between
China and USA is possible.

We consider the problem of optimal pressure and energy
control of water distribution systems without storage capacity.
We formulate the problem as a nonlinear, nonconvex programand employ an interior point algorithm (IPOPT) for its efficientsolution. We present our results from a case study using a large
scale, real network from UK.

Asiye Aydilek, Gulf University for Science and Technology (joint
work with Harun Aydilek)
Do we really need heterogenous agent models under recursive utility?

Modern combustion engines incorporate a number of actuators and sensors that can be used to control and optimize
the performance and emissions. We describe a semi-automatic
method to simultaneously measure and calibrate the actuatorsettings and the resulting behavior of the engine. The method
includes an adaptive process for refining the measurements, adata cleaning step, and an optimization procedure. The combination of these techniques significantly reduces the numberof measurements required to achieve a given set of calibration
goals. We demonstrate our method on practical examples.

We investigate the existence of representative agent undervarious heterogeneities in a recursive utility framework. We
provide the analytical solution of household allocations. We
numerically explore whether we can find a representative agent
whose income is the aggregate income of the society andwhose allocations are the aggregate allocations of the society
under heterogeneity in the parameter of risk aversion and/or
parameter of intertemporal substitution, or discount rate or
survival probability. We find that there is no representat
Po-An Chen, National Chiao Tung University (joint work with
Chi-Jen Lu, Yu-Sin Lu)
An Alternating Algorithm for Finding Linear Arrow-Debreu
Market Equilibrium
Jain reduced equilibrium computation in Arrow-Debreu (AD)markets to that in bijective markets. Motivated by the convergence of mirror-descent algorithms to market equilibria in linear
Fisher markets, we simply design algorithms to solve a rational
convex program for linear bijective markets in this paper. Ouralgorithm for computing linear AD market equilibrium is basedon solving the rational convex program formulated by Devanur
et al., repeatedly alternating between a step of gradient-descentlike updates and a follow-up step of optimization. Convergencecan be achieved by a new analysis.

Manuel Radons, TU Berlin (joint work with Timo Burggraf,
Michael Joswig, Marc Pfetsch, Stefan Ulbrich)Semi-automatically optimized calibration of internal combustion engines

Thu.2 10:45–12:00

Parallel Sessions Abstracts Thu.2 10:45–12:00

Yuxin Chen, Princeton University
Noisy Matrix Completion: Bridging Convex and Nonconvex Optimization
This talk is about noisy matrix completion. Arguably one of
the most popular paradigms is convex relaxation, which achieves
remarkable efficacy in practice. However, the theoretical supportof this approach is still far from optimal. We make progress
towards demystifying the practical efficacy of convex relaxation
vis-à-vis random noise. When the rank of the matrix is O(1), we
demonstrate that convex programming achieves near-optimal
estimation errors. All of this is enabled by bridging convex
relaxation with nonconvex optimization, a seemingly distinct
algorithm that is robust against noise.
Zhuoran Yang, Princeton University
Variational Transport: A Convergent Particle-Based Algorithm for Distributional Optimization
We consider the problem of minimizing a functional definedover a family of probability distributions, where the functional
is assumed to have a variational form. For this problem, we propose the variational transport algorithm, which approximately
performs functional gradient descent over the manifold of probability distributions via iteratively pushing a set of particles.
By characterizing both the statistical error of estimating thefunctional gradient and the progress of optimization algorithm,
we show that the proposed algorithm enjoys global convergencewith high probability.
Alfonso Lobos, University of California, Berkeley (joint workwith Paul Grigas, Nathan Vermeersch)
Stochastic In-Face Frank-Wolfe Methods for Non-Convex
Optimization and Sparse Neural Network Training
The Frank-Wolfe (FW) method and its extensions are wellsuited for delivering solutions with desirable structural properties, such as sparsity or low-rank structure. We adapt themethodology of in-face directions within the FW method tothe setting of non-convex optimization. We are particularly motivated by the application of stochastic versions of FW and theextensions mentioned above to the training of neural networks
with sparse and/or low-rank properties. We develop theoreticalcomputational guarantees, and we complement these resultswith extensive numerical experiments.

BIG
Thu.2 H 0110
Recent Advances in Optimization Techniques for LargeScale Structured Problems
Organizer: Akiko Takeda
Chair: Akiko Takeda
Zirui Zhou, Hong Kong Baptist University (joint work with
Zhaosong Lu)
Nonmonotone Enhanced Proximal DC Algorithms for aClass of Structured Nonsmooth DC Programming
In this talk we propose two nonmonotone enhanced proximal
DC algorithms for solving a class of structured nonsmoothdifference-of-convex (DC) minimization, in which the first convex component is the sum of a smooth and a nonsmoothfunction while the second one is the supremum of finitely
many smooth convex functions. We show that any accumulation point of the generated sequence is a D-stationary point.
We next propose randomized counterparts for them and showthat any accumulation point of the generated sequence is a
D-stationary point almost surely. Numerical results shall also
be presented.
Michael R. Metel, RIKEN AIP (joint work with Akiko Takeda)
Stochastic proximal gradient methods for non-smooth nonconvex optimization
Our work focuses on stochastic proximal gradient methods
for optimizing a smooth non-convex loss function with a nonsmooth non-convex regularizer and convex constraints. To thebest of our knowledge we present the first non-asymptotic
convergence results for this class of problem. We present two
simple stochastic proximal gradient algorithms, for finite-sum
and general stochastic optimization problems, which have thesame or superior convergence complexities compared to thecurrent state-of-the-art for the unconstrained problem setting.
Pierre-Louis Poirion, RIKEN Institute (joint work with Bruno
Figueira Lourenço, Akiko Takeda)
Random projections for conic LP
In this paper we propose a new random projection methodthat allows to reduce the number of inequalities of a Linear
Program (LP). More precisely, we randomly aggregate theconstraints of a LP into a new one with much fewer constraints,
while preserving, approximatively, the value of the LP. We will
also see how to extend this to conic linear programming.

Thu.2 10:45–12:00

BIG
Thu.2 H 0105
Nonconvex Optimization in Machine Learning (Part I)
Organizer: Ethan Fang
Chair: Ethan Fang

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BIG
Thu.2 H 3007
Big Data and Machine Learning - Contributed Session II
Chair: Rachael Tappenden
Lei Zhao, Shanghai Jiao Tong University (joint work with Daoli
Zhu)
Linear Convergence of Variable Bregman Stochastic Coordinate Descent Method for Nonsmooth Nonconvex Optimization by Level-set Variational Analysis
In this paper, we develop a new variational approach on levelsets aiming towards linear convergence of a variable Bregman
stochastic coordinate descent (VBSCD) method for a broadclass of nonsmooth and nonconvex optimization problem. Alongthis way, we not only derive the convergence of VBSCD, thatis any accumulation of the sequence generated by VBSCD isa critical point, but also provide {Eξk−1 F (xk )} Q−linear andalmost surely {Eξk−1 xk } R−linear convergence rate.
Sarit Khirirat, Division of Decision and Control (joint workwith Mikael Johansson, Sindri Magnússon)
Compressed Gradient Methods with Memory-Based Error
Compensation
The veritable scale of modern data necessitates information
compression in distributed optimization for machine learning.
Gradient compression schemes using memory-based error compensation have displayed superior performance in practice. We
illustrate how these approaches can be improved by using Hessian information in the compression. Explicit expressions for theaccuracy gains on strongly convex and non-convex optimization
problems are derived. The superiority of our approach in termsof convergence speed and solution accuracy is illustrated innumerical examples with 1-bit compression.
Rachael Tappenden, University of Canterbury (joint work withNaga Venkata C. Gudapati, Majid Jahani, Chenxin Ma, Martin
Takáč)Underestimate Sequences and Quadratic Averaging
This talk discusses several first order methods for minimizing
strongly convex functions in both the smooth and composite cases. The algorithms, based on efficiently updating lower
bounds on the objective functions, have natural stopping conditions that provide the user with a certificate of optimality.
Convergence of all algorithms is guaranteed via a new Underestimate Sequence framework, and the algorithms converge
linearly, with the accelerated variants converging at the optimallinear rate.

VIS
Thu.2 H 3013
Numerical Methods for Bilevel Optimization and Applications (Part I)
Organizer: Alain Zemkoho
Chair: Alain Zemkoho
Anthony Dunn, University of Southampton
Foam prediction to improve biogas production using anaerobic digestion
Anaerobic digestion is used in renewable energy plants to
produce biogas from recycled biological waste. Within thereaction digester, a phenomenon known as foaming can occur
at irregular time intervals which results in a reduced yield of
biogas and requires an extensive repair period. We show thatmachine learning based techniques (relying on single and twolevel optimization) allow for the development of a predictive
maintenance model that recognises periods of high foam risk.
This enables the plant operators to apply suitable changes in
the operations system to reduce the risk
Andrey Tin, University of Southampton
Gauss-Newton-type method for bilevel optimization
This article examines the application of Newton-type methods for overdetermined systems to find solutions of bilevel
programming problems. Since the Jacobian of the system is
non-square, we discuss Gauss-Newton method and Newton
method with Moore-Penrose pseudo inverse to solve the system. To our knowledge, this is the first time these methods
are discussed in literature to solve bilevel programs. We provethat the methods can be well-defined for the introduced optimality conditions. In numerical part we compare the resultsof the methods with known solutions and performance of 124
examples.
Alain Zemkoho, University of Southampton (joint work with
Yekini Shehu, Phan Tu Vuong)
An inertial extrapolation method for convex simple bilevel
optimization
We consider a scalar objective minimization problem overthe solution set of another optimization problem. This problem
is known as simple bilevel optimization problem and has drawn
a significant attention in the last few years. We propose andestablish the convergence of a fixed-point iterative methodwith inertial extrapolation to solve the problem. Our numericalexperiments show that the proposed method outperforms thecurrently known best algorithm to solve the class of problem
considered.

Thu.2 10:45–12:00

Parallel Sessions Abstracts Thu.2 10:45–12:00

Joachim Dahl, Joachim Dahl
A primal-dual algorithm for expontial-cone optimization
We discuss a primal-dual algorithm for expontial-cone optimization. The algorithm is based on primal-dual scaling proposed by Tuncel, with similar properties as Nesterov-Todd
scalings for symmetric cones. We also discuss a new corrector
for nonsymmetric cones, which considerably reduces the number of iterations required to solve a number of test problems.
This algorithm has recently been developed for version 9 of
the MOSEK software.
Alex Wang, Carnegie Mellon University (joint work with Fatma
Kilinc-Karzan)
A Linear-Time Algorithm for Generalized Trust Region
Subproblem based on a Convex Quadratic Reformulation
We consider the generalized trust region subproblem (GTRS)
of minimizing a nonconvex quadratic objective over a nonconvex
quadratic constraint. An obvious lifting of this problem recasts
the GTRS as minimizing a linear objective subject to two
nonconvex quadratic constraints. In this talk, we give an explicit
characterization of the convex hull of this nonconvex region andshow how it leads to a linear time (in the input size) algorithm
for approximating the GTRS.
Julio C Goez, NHH Norwegian School of Economics
Disjunctive conic cuts: the good, the bad, and implementation [canceled]
We discuss a generalization of Balas disjunctive cuts which
introduces the concept of disjunctive conic cuts (DCCs) anddisjunctive cylindrical cuts (DCyCs). The first part of this talkwill summarize the main results about DCCs and DCyCs including some results about valid conic inequalities for hyperboloids
and non-convex quadratic cones. In the second part, we will
discuss some of the limitation of this approach to derive usefulvalid inequalities in the context of MISOCO. In the last part we
explore the potential for implementation of DCCs and DCyC.

CON
Thu.2 H 2033
Polynomial Optimization Algorithms, Software, and Applications
Organizer: Etienne de Klerk
Chair: Anders Eltved
Brais González Rodrı́guez, Universidade de Santiago de Compostela (joint work with Julio González Dı́az, Ángel M. González
Rueda, Joaquı́n Ossorio Castillo, Diego Rodrı́guez Martı́nez,
David Rodrı́guez Penas)
RAPOSa, a freely available solver for polynomial optimization problems
We will present a new implementation of the reformulation
linearization techniques, RLT, in the context of polynomial
programming problems, originally introduced in Sherali andTuncbilek (1991). The implementation incorporates most of
the features of the RLT algorithm discussed in past literature.
Moreover, additional enhancements have been introduced, suchas parallelization and warm start features at various levels of
the branching process. The current version of the algorithm
has proven to be very efficient, and comparisons with otherglobal optimization solvers will be presented.
Yi-Shuai Niu, Shanghai Jiao Tong University (joint work with
Ya-Juan Wang)
Higher-order moment portfolio optimization via DC programming and sums-of-squares
We are interested in solving higher-order moment portfolio optimization via DC (Difference of Convex) programming
approach. The problem can be rewritten as a polynomial optimization which is equivalent to a DC program based on the
Difference-of-Convex-Sums-of-Squares (DCSOS) decomposition. The later problem can be solved by DCA. We also propose
an improved DC algorithm called Boosted DCA (BDCA). The
main idea of BDCA is to introduce a line search based on Armijo
typo rule to accelerate the convergence of DCA. Numericalsimulation results show good performance of our methods.
Anders Eltved, Technical University of Denmark (joint workwith Martin S. Andersen)
Exact Relaxation of Homogeneous Quadratically Constrained Quadratic Programs with Tree Structure
We study the semidefinite relaxation of homogeneous nonconvex quadratically constrained quadratic programs where
the matrices have a specific sparsity pattern. In particular, weconsider problems where the graph of the aggregate nonzero
structure of the matrices is a tree. We present a priori sufficientconditions for the semidefinite relaxation to be exact, which
implies that the original problem is solvable in polynomial time.
Checking these conditions requires solving a number of second
order cone programs based on the data in the matrices, which
can be done in polynomial time.

Thu.2 10:45–12:00

CON
Thu.2 H 2032
Advances in Algorithms for Conic Optimization
Organizer: Julio C Goez
Chair: Joachim Dahl

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CON

CNV
Thu.2 H 1028
Splitting Methods and Applications (Part II)
Organizers: Francisco Javier Aragón Artacho, Matthew K. Tam
Chair: Francisco Javier Aragón Artacho

Chee Khian Sim, University of Portsmouth
Polynomial complexity and local convergence of an interior
point algorithm on semi-definite linear complementarity
problems

C.H. Jeffrey Pang, National University of Singapore
Deterministic distributed asynchronous optimization with
Dykstra’s splittingThu.2 H 2038
Algorithms for Conic Programming
Organizer: Etienne de Klerk
Chair: Konrad Schrempf

We consider an infeasible predictor-corrector primal-dual
path following interior point algorithm to solve semi-definite
linear complementarity problems (SDLCPs). The well-known
Nesterov-Todd (NT) search direction is used in the algorithm.
We provide the best known polynomial complexity when thealgorithm is used to solve SDLCPs, and also give two sufficientconditions for when local superlinear convergence of iterates
generated by the algorithm occurs.
Kresimir Mihic, University of Edinburgh (joint work with Jacek
Gondzio, Spyridon Pougkakiotis)
Randomized multi-block ADMM based iterative methods
for solving large scale coupled convex problems
In this work we propose an ADMM based framework for solving large scale quadratic optimization problems. We focus on
non-separable dense and huge-scale sparse problems for which
the current interior point methods do not bring satisfactory
performance in terms of run time or scalability. We show thatby integrating first and second order methods we can achieve asignificant boost in performance with a limited loss in solution
quality.
Konrad Schrempf, University of Applied Sciences Upper Austria
Primal “non-commutative” interior-point methods forsemidefinite programming
Going over from the continuous (analytical) to the discrete(applied) setting one can use an “algebraic barrier” to get afamily of (primal) “non-commutative” feasible-interior-point
methods which are in particular useful for polynomial optimization (using sum-of-squares). I’m going to summarize the mainideas of my recent preprint arxiv:1812.10278 and show how analgebraic center can be used in different ways to find “good”
minimization directions.

Consider the setting where each vertex of a graph has afunction, and communications can only occur between vertices
connected by an edge. We wish to minimize the sum of thesefunctions. For the case when each function is the sum of astrongly convex quadratic and a convex function, we propose
a distributed version of Dykstra’s algorithm. This algorithm
is distributed, decentralized, asynchronous, has deterministic
convergence. We show how to extend to directed graphs withunreliable communications and some results on convergence
rates.
Pontus Giselsson, Lund University
Separate and Project: Nonlinear Forward-Backward Splitting with Projection Correction
We propose nonlinear forward-backward splitting with projection correction. The algorithm performs a forward-backward
step that creates a separating hyperplane between the current
point and the solution set. This is followed (if needed) by a correction step that projects onto the separating hyperplane. The
metric in the backward step is an arbitrary strictly monotone
single-valued mapping. This gives a very general method withknown special cases such as, AFBA, Bregman PDHG, NoLips,
FB(H)F, etc. Another special case is a novel four-operator
splitting method for monotone inclusions.
Adrien Taylor, Inria/ENS Paris (joint work with Carolina
Bergeling, Pontus Giselsson, Ernest Ryu)
Computer-aided proofs for operator splitting
We propose a methodology for automatically studying theperformance of common splitting methods. The methodology
enjoys comfortable tightness guarantees and relies on appropriate uses of semidefinite programming and/or symbolic computations. We illustrate the use of the framework for generatingtight contraction factors for Douglas–Rachford splitting thatare likely too complicated for a human to find bare-handed. We
show that the methodology can also be used for performing
optimal parameter selection.

Thu.2 10:45–12:00

Parallel Sessions Abstracts Thu.2 10:45–12:00

Nima Rabiei, American University of Iraq
AAR-Based Decomposition Method For Limit Analysis
The analysis of the bearing capacity of structures with arigid-plastic behaviour can be achieved resorting to computational limit analysis. Recent techniques have allowed scientists
and engineers to determine upper and lower bounds of theload factor under which the structure will collapse. Despite
the attractiveness of these results, their application to practical examples is still hampered by the size of the resulting
optimisation process.
Maryam Yashtini, Georgetown University (joint work with Sung
Ha Kang)High Quality Reconstruction with Euler’s elastica-based
methods
In this talk, I present numerical methods for solving Euler’selastica-regularized model for image processing. The minimizing
functional is non-smooth and non-convex, due to the presenceof a curvature term, which makes this model more powerfulthan the total variation-based models. Theoretical analysis
as well as various numerical results in image processing andmedical image reconstruction will be presented.
Jose Vidal-Nunez, TU Chemnitz (joint work with Ronny
Bergmann, Roland Herzog, Daniel Tenbrinck)
Fenchel duality for convex optimization on Riemannian
manifolds
This talk introduces a new duality theory that generalizes
the classical Fenchel-Legendre conjugation to functions defined on Riemannian manifolds. We present that results fromconvex analysis also hold for this novel duality theory on manifolds. Especially the Fenchel–Moreau theorem and properties
involving the Riemannian subdifferential can be stated in thissetting. A main application of this theory is that a specific classof optimization problems can be rewritten into a primal-dual
saddle-point formulation. This is a first step towards efficientalgorithms.

DER
Thu.2 H 3025
Recent Advances in(Part III)
Chair: Francesco Rinaldi

Derivative-Free

Optimization

Tyler Chang, Virginia Polytechnic Institute and State University(Virginia Tech) (joint work with Thomas Lux, Layne Watson)
A Surrogate for Local Optimization using Delaunay Triangulations
The Delaunay triangulation is an unstructured simplicial
mesh, which is often used to perform piecewise linear interpolation. In this talk, the Delaunay triangulation is presented
as a surrogate model for blackbox optimization. If the objective function is smooth, then the estimated gradient in eachsimplex is guaranteed to converge to the true gradient, andthe proposed algorithm will emulate gradient descent. Thisalgorithm is useful when paired with an effective global optimizer, particularly in situations where function evaluations are
expensive and gradient information is unavailable.
Alexander Matei, TU Darmstadt (joint work with Tristan Gally,
Peter Groche, Florian Hoppe, Anja Kuttich, Marc Pfetsch,
Martin Rakowitsch, Stefan Ulbrich)
Detecting model uncertainty via parameter estimation andoptimal design of experiments
In this talk we propose an approach to identify model uncertainty using both parameter estimation and optimal designof experiments. The key idea is to optimize sensor locations
and input configurations for the experiments such that theidentifiable model parameters can be determined with minimal
variance. This enables us to determine confidence regions, inwhich the real parameters or the parameter estimations from different test sets have to lie. We test the proposed method using
examples from load-carrying mechanical structures. Numericalresults are presented.
Douglas Vieira, Enacom (joint work with Adriano Lisboa,
Matheus de Oliveira Mendonça)Convergence properties of line search strategies for multimodal functions
It is presented line search optimization methods using amathematical framework based on the simple concept of av-pattern and its properties, providing theoretical guarantees
on preserving, in the localizing interval, a local optimum noworse than the starting point. The framework can be applied
to arbitrary unidimensional functions, including multimodal
and infinitely valued ones. Enhanced versions of the golden
section and Brent’s methods are proposed and analyzed within
this framework: they inherit the improving local optimality
guarantee.

Thu.2 10:45–12:00

CNV
Thu.2 H 1029
Convex and Nonsmooth Optimization - Contributed Session II
Chair: Jose Vidal-Nunez

159

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NON
Thu.2 H 0104
Recent Advances of First-Order Methods for Nonlinear
Problems (Part II)
Organizers: Shiqian Ma, Ion Necoara, Quoc Tran-Dinh,
Yangyang Xu
Chair: Yangyang Xu
Hao Yu, Alibaba Group (U.S.) Inc (joint work with Michael
Neely)A primal-dual parallel method with M CALO(1/) convergence for constrained composite convex programs
This work considers large scale constrained (composite) convex programs, which are difficult to solve by interior point methods or other Newton-type methods due to the non-smoothness
or the prohibitive computation and storage complexity for Hessians and matrix inversions. The conventional Arrow-HurwiczUzawa subgradient method is a parallel algorithm with O(1/2 )convergence for constrained convex programs that are possibly
non-smooth or non-separable. This work proposes a new primaldual parallel algorithm with faster M CALO(1/) convergencefor such challenging constrained convex programs.
Runchao Ma, University of Iowa (joint work with Qihang Lin)
Iteration Complexity for Constrained Optimization Problem under Local Error Bound and Weak Convexity
This paper considers the constrained optimization problem
with functional constraints. We first consider the case whenproblem is convex and has local error bound condition. Algorithms are proposed when condition number in local error
bound is either known or unknown. Iteration complexity isderived to show the benefit coming from local error property.
We then consider the case that constrained optimization problem is a weakly convex problem where both objective function
and constraint are weakly convex. Algorithm is proposed andcorresponding complexity is analyzed.
Yuyuan Ouyang, Clemson University
Lower complexity bounds of first-order methods for somemachine learning models
We consider a class of large-scale optimization problems
that arise from machine learning applications. In particular, theobjective function has rotational invariance with respective decision variables. We show that, given the machine learning model,
there exists worst-case datasets that yield lower complexity
bounds of first-order methods for the specified model.

NON
Thu.2 H 0106
Optimality Conditions in Nonlinear Optimization (Part II)
Chair: Paulo J. S. Silva
Olga Brezhneva, Miami University (joint work with Ewa
Szczepanik, Alexey Tret’yakov)
Necessary and sufficient optimality conditions for p-regularinequality constrained optimization problems
The focus of this talk is on nonregular optimization problems
with inequality constraints. We are interested in the case whenclassical regularity assumptions (constraint qualifications) arenot satisfied at a solution. We propose new necessary andsufficient optimality conditions for optimization problems withinequality constraints in the finite dimensional spaces. We
present generalized KKT-type optimality conditions for nonregular optimization problems using the construction of a p-factor
operator. The results are illustrated by some examples.
Maria Daniela Sanchez, Centro de Matemática de La Plata,
Universidad Nacional de La Plata (joint work with Nadia
Soledad Fazzio, Maria Laura Schuverdt, Raul Pedro Vignau)
Optimization Problems with Additional Abstract Set Constraints
We consider optimization problems with equality, inequality
and additional abstract set constraints. We extend the notionof Quasinormality Constraint Qualification for problems withadditional abstract set constraints. Also, we analyse the global
convergence of the Augmented Lagrangian method when thealgorithm is applied to problems with abstract set constraints.
Motivated by the results obtained, we also studied the possibility
to extend the definitions of others constraint qualifications wellknown in the literature for the case in which abstract set
constraints is considered.
S. K. Gupta, IIT Roorkee
Optimality conditions for a class of multiobjective intervalvalued fractional programming problem
The article aims to study the Karush-Kuhn-Tucker optimality condition for a class of a fractional interval multivalued
programming problem. For the solution concept, LU and LSPareto optimality are discussed, and some nontrivial examples
are illustrated. The concepts of LU-V-invex and LS-V-invex fora fractional interval problem are introduced and using theseassumptions, the Karush-Kuhn-Tucker optimality conditions
for the problem have been established. Non-trivial examples
are discussed throughout to make a clear understanding of theresults established.

Thu.2 10:45–12:00

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161

NON
Thu.2 H 2053
Methods of Optimization in Riemannian Manifolds(Part II)
Organizer: Orizon Pereira Ferreira
Chair: Orizon Pereira Ferreira

PDE
Thu.2 H 0112
Geometric Methods in Optimization of Variational Problems (Part II)
Organizers: Roland Herzog, Anton Schiela
Chair: Anton Schiela

Josua Sassen, University of Bonn (joint work with Behrend
Heeren, Klaus Hildebrandt, Martin Rumpf)
Geometric optimization using nonlinear rotation-invariant
coordinates

Andreas Weinmann, Hochschule Darmstadt
Reconstructing manifold-valued images using total variation regularization and related methods

Orizon Pereira Ferreira, Federal University of Goias (UFG)(joint work with Maurı́cio Louzeiro, Leandro Prudente)
Iteration-complexity and asymptotic analysis of steepest
descent method for multiobjective optimization on Riemannian manifolds
The steepest descent method for multiobjective optimization
on Riemannian manifolds with lower bounded sectional curvature is analyzed in this paper. The aim of the paper is twofold.
Firstly, an asymptotic analysis of the method is presented. The
second aim is to present iteration-complexity bounds for the
method with these three stepsizes. In addition, some examples
are presented to emphasize the importance of working in thisnew context. Numerical experiments are provided to illustrate
the effectiveness of the method in this new setting and certify
the obtained theoretical results.

In various problems in image processing, the data take values
in a nonlinear manifold. Examples are circle-valued data in SAR
imaging, special orthogonal group-valued data expressing vehicle headings, aircraft orientations or camera positions, motion
group data as well as shape-space data. A further example
is the space of positive matrices representing the diffusibilityof water molecules in DTI imaging. In this talk we consider
various variational approaches for such data including TV andhigher order methods.
Thomas Vogt, University of Lübeck
Measure-valued Variational Models for Non-Convex Imaging Problems
Many non-convex variational models from image processing
can be solved globally using convex relaxation frameworks.
A popular framework comes from the context of calibration
methods. We show that this framework can be interpreted asa measure-valued variational problem. This perspective can beapplied to scalar, vectorial and manifold-valued problems with
first- and second-order regularization.
Gladyston de Carvalho Bento, TU Chemnitz
Generalized optimality conditions for weak Pareto-optimal
solutions on Riemannian manifolds
It is known that optimality criteria form the foundations ofmathematical programming both theoretically and computationally. In this talk will be approach generalized optimality
conditions for weak Pareto-optimal solutions on Riemannian
manifolds which allowed to consider the proximal point method
for nondifferentiable multiobjective programs without any assumption of convexity over the constraint sets that determine
the vectorial improvement steps throughout the iterative process.

Thu.2 10:45–12:00

We consider Nonlinear Rotation-Invariant Coordinates(NRIC) representing triangle meshes with fixed combinatorics
as a vector stacking all edge lengths and dihedral angles. Previously, conditions for the existence of vertex positions matching
given NRIC have been established. We develop the machinery
needed to use NRIC for solving geometric optimization problems
and introduce a fast and robust algorithm that reconstructs
vertex positions from close-to integrable NRIC. Comparisons
to alternatives indicate that NRIC-based optimization is particularly effective for near-isometric problems.

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PDE
Thu.2 H 2013
PDE-constrained
Optimization
Under
Uncertainty(Part II)
Organizers: Harbir Antil, Drew Philip Kouri, Thomas Michael
Surowiec, Michael Ulbrich, Stefan Ulbrich
Chair: Harbir AntilThomas Michael Surowiec, Philipps-Universität Marburg (joint
work with Drew Philip Kouri)
A primal-dual algorithm for PDE-constrained optimization
under uncertainty
We propose a primal-dual algorithm for solving nonsmoothrisk-averse optimization problems in Banach spaces motivatedby PDE-constrained optimization under uncertainty. The overall algorithm is based on the well-known method of multipliers,
whereas the subproblem solves are based on inexact Newton
solvers for smooth PDE-constrained problems. The analysis
exploits a number of recent results on the epigraphical regularization of risk measures. We prove convergence of thealgorithm for solutions and stationary points and we conclude
with several illustrative numerical examples
Ekaterina Kostina, Heidelberg University
Robustness aspects in model validation for dynamic processes

ROB
Thu.2 H 3006
A Mixed Bag of Decision Problems
Organizer: Peyman Mohajerin Esfahani
Chair: Peyman Mohajerin Esfahani
Man-Chung Yue, Imperial College London (joint work with
Wolfram Wiesemann)
Optimistic Likelihood Problems Using (Geodesically) Convex Optimization
A fundamental problem arising in many areas of machinelearning is the evaluation of the likelihood of a given observation
under different nominal distributions. Frequently, these nominal
distributions are themselves estimated from data, which makes
them susceptible to estimation errors that are preserved or even
amplified by the likelihood estimation. To alleviate this issue,
we propose to replace the nominal distributions with ambiguity
sets containing all distributions that are sufficiently close to
the nominal distributions. When this proximity is measured by
the Fisher-Rao distance or the Kullback-Leibler divergence, theemerging optimistic likelihoods can be calculated efficiently using either geodesic or standard convex optimization techniques.
We showcase the advantages of our optimistic likelihoods ona classification problem using artificially generated as well as
standard benchmark instances.

Development and quantitative validation of complex nonlinear dynamic models is a difficult task that requires the support
by numerical methods for parameter estimation, and the optimal design of experiments. In this talk special emphasis isplaced on issues of robustness, i.e. how to take into accountuncertainties – such as outliers in the measurements for parameter estimation, and the dependence of optimal experiments
on unknown values of model parameters. New numerical methods will be presented, and applications will be discussed thatindicate a wide scope of applicability of the methods.

Bart P.G. Van Parys, Massachusetts Institute of Technology(joint work with Dimitris Bertsimas)
Data Analytics with B(R)agging Prediction Models.

Andreas Van Barel, KU Leuven (joint work with Stefan Vandewalle)Multilevel Quasi-Monte Carlo for stochastic PDE constrained optimization

Zhi Chen, City University of Hong Kong (joint work with
Zhenyu Hu, Qinshen Tang)
Allocation Inequality in Cost Sharing Problem

This talk explores the use of multilevel quasi-Monte Carlo
methods to generate estimations of gradients and Hessianvector products for the optimization of PDEs with random coefficients. In ideal circumstances, the computational effort may
scale linearly with the required accuracy, instead of quadratically, as is the case for Monte Carlo based methods. The
performance is tested for a tracking type robust optimization
problem constrained by an elliptic diffusion PDE with lognormal
diffusion coefficient.

We discuss prescribing optimal decisions in a framework
where their cost depends on uncertain problem parametersthat need to be learned from supervised data. Any naive useof training data may, however, lead to gullible decisions overcalibrated to one particular data set. In this presentation, we
describe an intriguing relationship between distributional robust
decision making and a bagging learning method by Breiman.

We consider the cost sharing problem where a coalition of
agents, each endowed with an input, shares the output cost
incurred from the total coalitional input. Inventory poolingunder ambiguous demand with known mean and covariance isa typical example of this problem. We bridge two important
allocations-average cost pricing and the Shapley value-fromthe angle of allocation inequality. We use the concept of majorization to characterize allocation inequality and we derive
simple conditions under which one allocation majorizes theother.

Thu.2 10:45–12:00

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163

SPA
Thu.2 H 1012
Continuous Systems in Information Processing (Part II)
Organizer: Martin Benning
Chair: Daniel Tenbrinck
Jan Lellmann, University of Lübeck
Measure-Valued Variational Image Processing
Many interesting image processing problems can be solved
by finding suitable minimizers of a variational model. In thistalk, we consider models where the unknowns reside in a spaceof measures. We will consider theoretical results and discuss
some applications, in particular in diffusion-weighted imaging,manifold-valued optimization and approximation of non-convex
problems.
Noémie Debroux, University of Cambridge (joint work withAngelica Aviles-Rivero, Veronica Corona, Martin Graves, Carole
Le Guyader, Carola-Bibiane Schönlieb, Guy Williams)
A unified variational joint reconstruction and registration
model for motion-compensated MRIThis work addresses a central topic in Magnetic ResonanceImaging (MRI) which is the motion-correction problem in ajoint reconstruction and registration framework. From a setof multiple MR acquisitions corrupted by motion, we aim at
jointly reconstructing a single high quality motion-free corrected image and retrieving the physiological dynamics throughthe deformation maps. To this purpose, we propose a novel
variational model relying on hyperelasticity and compressedsensing principles.
Daniel Tenbrinck, Friedrich-Alexander-Universität ErlangenNürnberg (FAU) (joint work with Martin Burger, Fjedor Gaede)
Variational Graph Methods for Efficient Point Cloud Sparsification

Thu.3 13:30–14:45

In this talk we present an efficient coarse-to-fine optimization
strategy for sparsification of large point clouds modeled via
finite weighted graphs. Our method is derived from the recently
proposed Cut Pursuit strategy and leads to a significant speedup (up to factor 120) in computing optimizers for a family of
related variational problems. We demonstrate results on both
unperturbed as well as noisy 3D point cloud data.

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Thu.3 13:30–14:45
APP
Thu.3 H 3004
Other Applications
Chair: Jean-Paul Arnaout
Sugandima Mihirani Vidanagamachchi, University of Ruhuna(joint work with Shirly Devapriya Dewasurendra)
A Reconfigurable Architecture for Commentz-Walter Algorithm
This work describes the hardware implementation ofCommentz-Walter algorithm to match multiple patterns inprotein sequences. Multiple pattern matching for different applications with the most widely used Aho-Corasick algorithm
has been carried out over the years by PCs as well as GPUs
and FPGAs. However, due to the complexity of CommentzWalter algorithm, it has not been implemented in any hardware
platform except in PCs. In this work, a specific architecture
for this task using Commentz-Walter algorithm has been developed, tested and implemented on an FPGA to efficiently
match proteins.
Yidiat Omolade Aderinto, University of Ilorin (joint work with
Issa Temitope Issa)
On Mathematical Model of Biological Pest Control ofSorghum Production
In this paper, mathematical model of Sorghum production
from seed/planting to harvesting stage was presented withrespect to its pests(prey) and the corresponding natural prey
enemy (predators) at every stage. The model was characterized,
the existence and uniqueness of the model solution was established. And finally numerical applications was carried out using
Differential Transform method, and it was found that pests
at different stages of sorghum production can be minimized
below injury level using biological control which in turn leads
to maximization of the sorghum production.
Jean-Paul Arnaout, Gulf University for Science & Technology
Worm Optimization Algorithm for the Euclidean LocationAllocation Problem
Facility location is a critical aspect of strategic planning
for a broad spectrum of public and private firms. This study
addresses the Euclidean location-allocation problem with an
unknown number of facilities, and an objective of minimizing
the fixed and transportation costs. This is a NP-hard problem
and in this paper, a worm optimization (WO) algorithm isintroduced and its performance is evaluated by comparing it to
Genetic Algorithms (GA) and Ant Colony Optimization (ACO).
The results show that WO outperformed ACO and GA andreached better solutions in a faster computational time.

BIG
Thu.3 H 0105
Canceled! Nonconvex Optimization in Machine Learning(Part II)
Organizer: Ethan Fang
Chair: Ethan Fang
Stephane Chretien, National Physical Laboratory (joint workwith Assoweh Mohamed Ibrahim, Brahim Tamadazte)
Low tubal rank tensor recovery using the Burer-Monteiro
factorisation approach [canceled]
In this paper, we study the low-tubal-rank tensor completion
problem, i.e., to recover a third-order tensor by observing a
subset of elements selected uniformly at random. We propose
a fast iterative algorithm, called Tubal-Alt-Min. The unknown
low-tubal-rank tensor is parameterised as the product of twomuch smaller tensors which naturally encode the low-tubal-rank
property. Tubal-Alt-Min alternates between estimating those
two tensors using least squares minimisation. We establish
a “local is global” property for the minimisers, which implies
convergence of Tubal-Alt-Min to global solutions.
Ke Wei, Fudan University (joint work with Jian-Feng Cai, Tianming Wang)
Nonconvex Optimization for Spectral Compressed Sensing[canceled]
Spectral compressed sensing is about reconstructing spectrally sparse data from incomplete information. Two differenceclasses of nonconvex optimization algorithms are introduced
to tackle this problem. Theoretical recovery guarantees will bepresented for the proposed algorithms under certain random
models, showing that the sampling complexity is essentially
proportional to the intrinsic dimension of the problems rather
the ambient dimension. Empirical observations demonstrate
the efficacy of the algorithms.

Thu.3 13:30–14:45

Parallel Sessions Abstracts Thu.3 13:30–14:45

Qing Qu, New York University
Short-and-Sparse Deconvolution – A Geometric Approach
The problem of locating repetitive unknown motifs in signals
appears ubiquitously in neuroscience, computational imaging,microscopy data analytics, and more. We cast this problem asa short-and-sparse blind deconvolution problem, and formulate
it as a nonconvex optimization problem over the sphere. By
studying the geometric structure of the nonconvex landscape,we show how to develop practical optimization methods, thatsolves the problem to the target solution up to a shift ambiguity.
The new geometric insights lead to new optimization strategies
that even works in very challenging regimes
Cheolmin Kim, Northwestern University (joint work with
Youngseok Kim, Diego Klabjan)
Scale Invariant Power Iteration
Several machine learning models can be formulated as maximization of a scale invariant function under a Euclidian ballconstraint, for example, PCA, GMM, NMF. We generalize power
iteration to this setting and analyze convergence properties of
the algorithm. Our main result states that if initialized close
to a local maximum, then the algorithm converge to this local
maximum. Also, the convergence rate is linear and thus equal
to the rate of power iteration. Numerically, we benchmark thealgorithm against state-of-the-art methods to find out that thealgorithm outperforms benchmark algorithms.
Dimosthenis Pasadakis, Università della Svizzera italiana (USI)(joint work with Drosos Kourounis, Olaf Schenk)
Spectral Graph Partition Refinement using the Graph pLaplacian
A continuous reformulation of the traditional spectral
method for the bi-partitioning of graphs is presented, that
exploits the tendency of the p-norm to promote sparsity, thus
leading to partitions with smaller edgecuts. The resulting nonlinear and non convex graph p-Laplacian quotient minimizationis handled with an accelerated gradient descent, while a continuation approach reduces the p-norm from a 2-norm towards
a 1-norm. The benefits of our scheme are demonstrated in aplethora of complex graphs emerging from power networks andsocial interactions.

VIS
Thu.3 H 3013
Numerical Methods for Bilevel Optimization and Applications (Part II)
Organizer: Alain Zemkoho
Chair: Alain Zemkoho(FAU)
Thomas Kleinert, Friedrich-Alexander-UniversitätErlangen-Nürnberg (joint work with Martin Schmidt)
Computing Stationary Points of Bilevel Reformulations
with a Penalty Alternating Direction Method
Bilevel problems are highly challenging optimization problems
that appear in many applications. Due to the hierarchicalstructure, they are inherently non-convex. In this talk we present
a primal heuristic for (MI)QP-QP bilevel problems based on apenalty alternating direction method. We derive a convergence
theory stating that the method converges to a stationary pointof an equivalent single-level reformulation of the bilevel problem.
Further, we provide an extensive numerical study and illustrate
the good performance of our method both in terms of runningtimes and solution quality.
Tangi Migot, University of Guelph
On a regularization-active set algorithm to solve the
MPCC and its implementation
We consider here the mathematical program with complementarity constraints (MPCC). In a recent work by Dussault
et al. [How to compute an M-stationary point of the MPCC],
the authors present an algorithmic scheme based on a generalfamily of regularization for the MPCC with guaranteed convergence to an M-stationary point. This algorithmic scheme
is a regularization-penalization-active set approach, and thus,
regroups several techniques from nonlinear optimization. Our
aim in this talk is to discuss this algorithmic scheme and several
aspects of its implementation in Julia.
Jie Jiang, Xi’an Jiaotong University
Quantitative analysis for a class of two-stage stochastic
linear variational inequality problems
This talk considers a class of two-stage stochastic linear
variational inequality problems. We first give conditions forthe existence of solutions to both the original problem and its
perturbed problems. Next we derive quantitative stability assertions of this two-stage stochastic problem under total variation
metrics via the corresponding residual function. After that, westudy the discrete approximation problem. The convergenceand the exponential rate of convergence of optimal solution
sets are obtained under moderate assumptions respectively.

Thu.3 13:30–14:45

BIG
Thu.3 H 3007
Big Data and Machine Learning - Contributed Session III
Chair: Dimosthenis Pasadakis

165

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Parallel Sessions Abstracts Thu.3 13:30–14:45

CON
Thu.3 H 2033
Miscellaneous Topics in Conic Programming
Organizer: Etienne de Klerk
Chair: Sandra S. Y. Tan
Antonios Varvitsiotis, National University of Singapore (NUS)
Robust self-testing of quantum systems via noncontextuality inequalities
Self-testing unknown quantum systems is a fundamental problem in quantum information processing. We study
self-testing using non-contextuality inequalities and showthat the celebrated Klyachko-Can-Binicioğlu-Shumovsky noncontextuality tests admit robust self-testing. Our results rely
crucially on the graph-theoretic framework for contextuality
introduced by Cabello, Severini, and Winter, combined withwell-known uniqueness and robustness properties for solutions
to semidefinite programs.
Lucas Slot, CWI (joint work with Monique Laurent)
Convergence analysis of measure-based bounds for polynomial optimization on compact sets
We investigate a hierarchy of upper bounds introduced by
Lasserre (2011) for the minimization of a polynomial f over aset K, obtained by searching for a degree 2r sos density function
minimizing the expected value of f over K with respect toa given measure. For the hypercube, de Klerk and Laurent(2018) show that the convergence rate of these bounds to theglobal minimum is in O(1/r2 ). We extend this result to a large
subclass of convex bodies. For general compact K, we show aconvergence rate in O(log r/r) under a minor condition and arate in O((log r/r)2 ) when K is a convex body.
Sandra S. Y. Tan, NUS (joint work with Vincent Y. F. Tan,
Antonios Varvitsiotis)
A Unified Framework for the Convergence Analysis of Optimization Algorithms via Sum-of-Squares
Given the popularity of machine learning, understanding theconvergence properties of the optimization algorithms is oftheoretical importance. However, existing convergence proofs
consist mostly of ad-hoc arguments. We introduce a novel
framework for bounding the convergence rates of various algorithms by means of sum-of-squares certificates, thereby unifying
their convergence analyses. Using our approach, we recover
known convergence bounds for three widely-used first-orderalgorithms, putting forth a promising framework for unifying
the convergence analyses of other optimization algorithms.

CON
Thu.3 H 2038
Conic Optimization Approaches to Discrete Optimization
Problems
Organizer: Etienne de Klerk
Chair: Enrico Bettiol
Fatima Ezzahra Khalloufi, Faculty of science of Agadir, Ibn
Zohr University (joint work with Rachida Abounacer, Mohamed
Hachimi)The novel Improvement of Clarke and Wright algorithm
for solving Capacitated Vehicle Routing problem
We propose an algorithm that has been improved from theclassical Clarke and Wright algorithm to solve the Capacitatedvehicle routing problem.The main concept of our proposed
algorithm is to combine the Clarke and Wright heuristic with
Sweep heuristic inspired of transition of probabilities used in
Ant Colony algorithm to construct a solution.The objective
is to seek a set minimum total cost routes for a fleet of kidentical vehicle with capacity D to serve n customers form adepot .Our results shows that our approach provides shorter
distances in the majority of well-know instances.
Timotej Hrga, University of Ljubljana (joint work with Borut
Lužar, Janez Povh)
Parallel Branch and Bound algorithm for Stable Set problem
We present a method for solving stable set problem to optimality that combines techniques from semidefinite optimizationand efficient implementation using high-performance computing. We use parallel branch & bound algorithm that applies
Lovász theta function and its strengthenings as upper boundto approximate original problem using semidefinite relaxation.
Depending on the sparsity of the graph combination of two
SDP solvers is used to compute solutions of the obtained relaxations. We will present numerical experience with the proposed
algorithm and show scalability and efficiency of it.
Enrico Bettiol, Université Paris 13 (joint work with ImmanuelBomze, Lucas Létocart, Francesco Rinaldi, Emiliano Traversi)
A CP relaxation for block-decomposable Binary QCQPs
via Column Generation
We propose an algorithm for binary non-convex quadraticallyconstrained quadratic problems. In the extended formulation
with the matrix X, product of the original variables, we proposea Dantzig-Wolfe-reformulation-based method with a linear
master and a pricing of Max-Cut-type. The domain of thisrelaxation lies in the cone of Completely Positive matrices.
For block-decomposable problems, we present an adaptationof our algorithm which exploits the block structure. We giveconditions under which we obtain the same bound as theprevious relaxation. We also provide computational results.

Thu.3 13:30–14:45

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167

CNV
Thu.3 H 1028
Splitting Methods and Applications (Part III)
Organizers: Pontus Giselsson, Ernest Ryu, Adrien Taylor
Chair: Ernest Ryu

CNVThu.3 H 1029
Convex and Nonsmooth Optimization - Contributed Session III
Chair: Andrew Eberhard

Puya Latafat, KU Leuven (joint work with Panagiotis Patrinos,
Andreas Themelis)
Block-coordinate and incremental aggregated proximal
gradient methods: A unified view

Ching-pei Lee, National University of Singapore / Universityof Wisconsin-Madison (joint work with Stephen Wright)(Non-accelerated) First-Order Algorithms Converge
Faster than O(1/k) on Convex Problems

We establish a link between the block-coordinate proximal
gradient for minimizing the sum of two nonconvex functions(none of which is necessarily separable) and incremental aggregated proximal gradient methods for finite sum problems.
The main tool for establishing this connection is the forwardbackward envelope (FBE), which serves as a Lyapunov function.
This result greatly simplifies the rate-of-convergence analysis
and opens up the possibility to develop accelerated variants ofincremental aggregated proximal gradient methods.

It has been known for many years that both gradient descentand stochastic coordinate descent achieve a global convergence
rate of O(1/k) in the objective value, when applied to a scheme
for minimizing a Lipschitz-continuously differentiable, unconstrained convex function. In this work, we improve this rate too(1/k). We extend the result to proximal gradient and proximal
stochastic coordinate descent with arbitrary samplings for thecoordinates on regularized problems to show similar o(1/k)
convergence rates.

Matthew K. Tam, University of Göttingen (joint work with
Yura Malitsky)
Splitting Algorithms with Forward Steps

Yan Gu, Kyoto University (joint work with Nobuo Yamashita)
An Alternating Direction Method of Multipliers with the
BFGS Update for Structured Convex Quadratic Optimization

Ernest Ryu, UCLA (joint work with Robert Hannah, Wotao
Yin)
Scaled Relative Graph: Nonexpansive operators via 2D Euclidean Geometry
Many iterative methods in applied mathematics can bethought of as fixed-point iterations, and such algorithms areusually analyzed analytically, with inequalities. In this paper,
we present a geometric approach to analyzing contractive andnonexpansive fixed point iterations with a new tool called thescaled relative graph (SRG). The SRG provides a rigorous
correspondence between nonlinear operators and subsets of the2D plane. Under this framework, a geometric argument in the2D plane becomes a rigorous proof of contractiveness of thecorresponding operator.

We consider a special proximal alternating direction methodof multipliers (ADMM) for the structured convex quadratic
optimization problem. In this work, we propose a proximal
ADMM whose regularized matrix in the proximal term is generated by the BFGS update (or limited memory BFGS) at every
iteration. These types of matrices use second-order informationof the objective function. The convergence of the proposed
method is proved under certain assumptions. Numerical results
are presented to show the effectiveness.
Andrew Eberhard, RMIT University (joint work with Shuai Liu,
Yousong Luo)
Partial Smoothness, Tilt Stability and the VU–
Decomposition
Under the assumption of prox-regularity and the presenceof a tilt stable local minimum we are able to show that a
VU like decomposition gives rise to the existence of a smoothmanifold on which the function in question coincides locallywith a smooth function. We will also consider the inverse
problem. If a fast track exists around a strict local minimum
does this imply the existence of a tilt stable local minimum?
We investigate conditions under which this is so by studying theclosely related notions of fast track and of partial smoothness
and their equivalence.

Thu.3 13:30–14:45

The forward-backward splitting algorithm is a method forfinding a zero in the sum of two monotone operators when
one assumed single-valued and cocoercive. Unfortunately, thelatter property, which is equivalent to strong monotonicity of
the inverse, is too strong to hold in many monotone inclusionsof interest. In this talk, I will report on recently discovered
modifications of the forward-backward splitting algorithm whichconverge without requiring the cocoercivity assumption. Basedon joint work with Yura Malitsky (University of Göttingen)168

Parallel Sessions Abstracts Thu.3 13:30–14:45

NON
Thu.3 H 0104
Advances in Nonlinear Optimization With Applications(Part II)
Organizers: Yu-Hong Dai, Deren Han
Chair: Yu-Hong Dai
Xingju Cai, Nanjing Normal University
An indefinite proximal point algorithm for maximal monotone operator
We investigate the possibility of relaxing the positive definiteness requirement of the proximal matrix in PPA. A new
indefinite PPA for finding a root of maximal monotone operator
is proposed via choosing an indefinite proximal regularization
term. The proposed method is more exible, especially in dealing
with cases where the operator has some special structures. We
prove the global convergence. We also allow the subproblem
to be solved in an approximate manner and propose two exible
inexact criteria.
Caihua Chen, Nanjing University
On the Linear Convergence of the ADMM for Regularized
Non-Convex Low-Rank Matrix Recovery
In this paper, we investigate the convergence behavior of
ADMM for solving regularized non-convex low-rank matrix
recovery problems. We show that the ADMM will converge
globally to a critical point of the problem without making
any assumption on the sequence generated by the method. If
the objective function of the problem satisfies the Lojasiewicz
inequality with exponent at every (globally) optimal solution,then with suitable initialization, the ADMM will converge linearly. We exhibit concrete instances for which the ADMM
converges linearly.
Deren Han,
On the convergence rate of a splitting augmented Lagrangian method [canceled]
In this talk, we present a new ADMM based predictioncorrection method (APCM) for solving a three-block convex
minimization problem with linear constrains. We solve thesubproblem partially parallel in the prediction step and only
the third variable and Lagrange multiplier are corrected in thecorrection step, which results in less time cost in the prediction
step and employs different step size in the correction step.
We show its global convergence and the O(1/t) convergence
rate under mild conditions. We also give the globally linear
convergence rate with some additional assumptions.

NON
Thu.3 H 0106
Nonlinear Optimization - Contributed Session III
Chair: Refail Kasimbeyli
Gulcin Dinc Yalcin, Eskisehir Technical University (joint workwith Refail Kasimbeyli)
Weak Subgradient Based Cutting Plane Method
In this study, we present a version of the Cutting-Plane
Method, developed for minimizing a nonconvex function. We
use weak subgradients which is based on the idea of supporting
conic surfaces. This supporting approach does not requireconvexity condition on the function. The suggested method,minimizes the pointwise maximum of superlinear functions, by
the weak subgradient. Since the graphs of supporting functions
are conical surfaces, the new method is called the “Cutting
Conic Surfaces Method”. The subproblem is “linearized” andsome illustrative examples are provided.
Acknowledgment : This study is supported by The Scientificand Technological Research Council of Turkey (TUBITAK)
under the Grant No. 217M487.
Rafael Durbano Lobato, Università di Pisa (joint work with
Antonio Frangioni)
SMS++: a Structured Modelling System for (Among Others) Multi-Level Stochastic Problems
The aim of the open-source Structured Modeling System++(SMS++) is to facilitate the implementation of general andflexible algorithms for optimization problems with several nested
layers of structure. We will present the current state of SMS++,focussing on recent developments concerning the representation of uncertainty, which are meant to facilitate the transformation of complex deterministic models into stochastic ones.
Specific SMS++ facilities for “general-purpose decomposition
techniques” will then allow to efficiently produce multi-level
decomposition approaches to these problems.
Refail Kasimbeyli, Eskisehir Technical University (joint workwith Gulcin Dinc Yalcin)
Weak subgradient based solution method using radial epiderivatives
This work presents a weak subgradient based solution method
in nonconvex unconstrained optimization. The weak subgradient notion does not require convexity condition on the function
under consideration, and hence allows to investigate a wide classof nonconvex optimization problems. By using relationships
between the weak subgradients and the radial epiderivatives, we
present a method for approximate estimation of weak subgradients, and use it to update a current solution. The convergence
theorems for the presented method, are established and theillustrative examples are provided.
Acknowledgment : This study is supported by The Scientificand Technological Research Council of Turkey (TUBITAK)
under the Grant No. 217M487.

Thu.3 13:30–14:45

Parallel Sessions Abstracts Thu.3 13:30–14:45

NON
Thu.3 H 0107
Variational Inequalities, Minimax Problems and GANs
(Part II)
Organizer: Konstantin Mishchenko
Chair: Konstantin Mishchenko
Daoli Zhu, Shanghai Jiao Tong University (joint work withSien Deng)
A Variational Approach on Level sets and Linear Convergence of Variable Bregman Proximal Gradient Method for
Nonconvex Optimization Problems
We provide various level-set based error bounds to study
necessary and sufficient conditions on the linear convergenceof variable Bregman proximal gradient (VBPG) method for abroad class of nonsmooth and nonconvex optimization problems.
We also provide a fresh perspective that allows us to explore
the connections among many known sufficient conditions forlinear convergence of various first-order methods.
Sarath Pattathil, MIT (joint work with Aryan Mokhtari,
Asuman Ozdaglar)
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point
Approach
In this talk, we consider solving convex-concave saddle point
problems. We focus on two variants of gradient decent-ascentalgorithms, extra-gradient (EG) and optimistic gradient descentascent (OGDA) methods, and show that they admit a unified analysis as approximations of the classical proximal point
method for solving saddle-point problems. This viewpoint enables us to generalize OGDA (in terms of parameters) andobtain new convergence rate results for these algorithms forthe bilinear case as well as the strongly convex-concave setting.

169

NON
Thu.3 H 2032
Nonlinear Optimization - Contributed Session II
Chair: Jourdain Lamperski
Simone Rebegoldi, Università degli Studi di Modena e Reggio
Emilia (joint work with Silvia Bonettini, Marco Prato)
Convergence analysis of an inexact forward-backward
scheme using the forward-backward envelope
We present an inexact variable metric linesearch based
forward-backward method for minimizing the sum of an analytic function and a subanalytic convex term. Upon the conceptof forward-backward envelope, we build a continuously differentiable surrogate function, coinciding with the objective function
at every stationary point and satisfying a relative error condition which might fail for the objective function. By exploiting
the Kurdyka-Lojasiewicz property on the surrogate, we prove
convergence of the iterates to a stationary point, as well as
the convergence rates for the function values.
Andrea Cristofari, Università di Padova
An almost cyclic 2-coordinate descent method for minimization problems with one linear equality constraint
A block decomposition method is proposed for minimizing a
non-convex continuously differentiable function subject to one
linear equality constraint and simple bounds on the variables.
The working set is computed according to an almost cyclic
strategy that does not use first-order information, so thatthe whole gradient of the objective function does not need
to be computed during the algorithm. Under an appropriate
assumption on the level set, global convergence to stationary
points is established by using different line search strategies.
Numerical results are finally provided.
Jourdain Lamperski, MIT
Solving Linear Inequalities via Non-convex Optimization

We study systems where such online algorithms, such as
online gradient descent and multiplicative weights, compete
against each other in zero-sum games. We prove that thesesystems exhibit Poincaré recurrence and can be interpreted
as Hamiltonian dynamics. We discuss implications of theseresults for discrete-time dynamics, designing new algorithms
with provable guarantees beyond convex-concave games andwe present some open questions.

We mainly consider solving homogeneous linear inequalities.
We formulate a non-convex optimization problem in a slightly
lifted space, whose critical points map to feasible solutionsof the linear inequalities. We show various properties of the
non-convex objective function and develop an algorithm thatcomputes critical points thereof, thus yielding an algorithm
for linear inequalities. We establish convergence guarantees
for the algorithm and further investigate its performance via
computational experiments.

Thu.3 13:30–14:45

Georgios Piliouras, SUTD
Online Optimization in Zero-Sum Games and Beyond: A
Dynamical Systems Approach

170

Parallel Sessions Abstracts Thu.3 13:30–14:45

NON
Thu.3 H 2053
Nonlinear Optimization - Contributed Session I
Chair: Raul Tempone
Truc-Dao Nguyen, Wayne State University (joint work with
Tan Cao, Giovanni Colombo, Boris Mordukhovich)
Discrete Approximations of a Controlled sweeping process
over polyhedral sets with perturbations
The talk is devoted to a class of optimal control problems
governed by a perturbed sweeping (Moreau) process with themoving convex polyhedral set, where the controls are used todetermine the best shape of the moving set in order to optimize
the given Bolza-type problem. Using the method of discrete
approximations together with the advanced tools of variational
analysis and generalized differentiation allows us to efficiently
derive the necessary optimality conditions for the discretized
control problems and the original controlled problem. Somenumerical examples are presented.
Adrian Hauswirth, ETH Zürich (joint work with Saverio Bolognani, Florian Dörfler, Gabriela Hug)
Timescale Separation in Autonomous Optimization
Autonomous optimization is an emerging concept in control
theory that refers to the design of feedback controllers thatsteer a physical system to a steady state that solves a predefined nonlinear optimization problem. These controllers aremodelled after optimization algorithms, but are implemented inclosed loop with the physical system. For this interconnection
to be stable, both systems need act on sufficiently different
timescales. We quantify the required timescale separation andgive prescriptions that can be directly used in the design ofsuch feedback-based optimization schemes.
Raul Tempone, RWTH Aachen (joint work with ChristianBayer, Juho Häppölä)
Pricing American options by Markovian projections
We consider the pricing American basket options in a multivariate setting, including the Black-Scholes, Heston and therough Bergomi model. In high dimensions, nonlinear PDEs
methods for solving the problem become prohibitively costly
due to the curse of dimensionality. We propose a stopping
rule depending on a low-dimensional Markovian projectionof the given basket of assets. We approximate the optimal
early-exercise boundary of the option by solving a HamiltonJacobi-Bellman PDE in the projected, low-dimensional space.
This is used to produce an exercise strategy for the original
option.

PDE
Thu.3 H 0112
Geometric Methods in Optimization of Variational Problems (Part III)
Organizers: Roland Herzog, Anton Schiela
Chair: Roland Herzog
Anton Schiela, Universität Bayreuth (joint work with Julian
Ortiz)An SQP method for equality constrained optimization on
manifolds
We present a composite step method, designed for equality
constrained optimization on differentiable manifolds. The useof retractions allows us to pullback the involved mappings
to linear spaces and use tools such as cubic regularization of
the objective function and affine covariant damped Newton
method for feasibility. We show fast local convergence whendifferent chart retractions are considered. We test our methodon equilibrium problems in finite elasticity where the stableequilibrium position of an inextensible transversely isotropic
elastic rod under dead load is searched.
Silvio Fanzon, University of Graz (joint work with KristianBredies)
Optimal transport regularization for dynamic inverse problems
We propose and study a novel optimal transport based regularization of linear dynamic inverse problems. The consideredinverse problems aim at recovering a measure valued curveand are dynamic in the sense that (i) the measured data takes
values in a time dependent family of Hilbert spaces, and (ii)the forward operators are time dependent and map, for eachtime, Radon measures into the corresponding data space. The
variational regularization we propose bases on dynamic optimaltransport. We apply this abstract framework to variational
reconstruction in dynamic undersampled MRI
Hanne Hardering, TU DresdenGeometric Finite Elements
We introduce a class of finite elements for the discretizationof energy minimization problems for manifold-valued maps. The
discretizations are conforming in the sense that their images
are contained in the manifold and invariant under isometries.
Based on this desireable invariance, we developed an a priori
error theory.

Thu.3 13:30–14:45

Parallel Sessions Abstracts Thu.3 13:30–14:45

Sergio Rodrigues, RICAM
Semiglobal exponential stabilization of nonautonomous
semilinear parabolic-like systems
It is shown that an explicit oblique projection nonlinear feedback controller is able to stabilize semilinear parabolic equations,with time-dependent dynamics and with a polynomial nonlinearity. The actuators are typically modeled by a finite numberof indicator functions of small subdomains. No constraint is
imposed on the norm of the initial condition. Simulations arepresented, which show the semiglobal stabilizing performanceof the nonlinear feedback.
Johannes Milz, Technical University of Munich (joint work withMichael Ulbrich)
An approximation scheme for distributionally robust optimization with PDEs
We consider distributionally robust optimization (DRO) for
uncertain discretized PDE-constrained problems. To obtaina tractable and an accurate lower-level problem, we define
ambiguity sets by moment constraints and approximate nonlinear functions using quadratic expansions w.r.t. parameters
resulting in an approximate DRO problem. Its cost function
is given as the sum of the value functions of a trust-region
problem and an SDP. We construct smoothing functions andshow global convergence of a homotopy method. Numericalresults are presented using the adjoint approach to computederivatives.

ROB
Thu.3 H 1058
Distributionally Robust Optimization:
Methodology and Applications
Organizer: Aurelie Thiele
Chair: Aurelie Thiele

AdvancesinSanjay Mehrotra, Northwestern University (joint work with
Jinlin Li, Shanshan Wang)
Distributionally Robust Chance-Constrained Assignment
with an Application to Operation Room Assignments
We will present a distributionally robust chance constraint
optimization model, where the probability distribution specifying the chance constraint is robustified using a Wassersteinambiguity set. For this problem, we will present novel techniques
for strengthening big-M type formulations, and techniques forgenerating inequalities for use within a branch-and-cut framework. Results based on experiments using real data from an
operating room will be presented, where the problem underconsideration is to assign surgeries to the operating room.
Tito Homem-de-Mello, Universidad Adolfo Ibanez (joint workwith Guzin Bayraksan, Hamed Rahimian)
Effective scenarios for multi-stage distributionally robustmodelsRecent research has studied the notion of effective scenarios
for static distributionally robust stochastic programs. Roughly
speaking, a scenario is deemed effective if its removal changes
the optimal value of the problem. In this presentation we discuss
the extension of these ideas to the case of multistage stochastic
programs. Such extension requires proper ways of defining
the meaning of removing a scenario in a dynamic context.
Computational and analytical results show that identifying such
effective scenarios may provide useful insight on the underlying
uncertainties of the problem.
Aurelie Thiele, Southern Methodist University (joint work withHedieh Ashrafi)
Robust continuous optimization for project portfolio management under uncertainty
We consider the management of projects under uncertainty ina firm’s market entry, competitors’ entry and product adoption
dynamics. The company can adjust manpower staffing on
a portfolio of projects (new products), which diversifies its
portfolio but delays market entry for each product line, making
it more likely a rival will launch a competing product first.
We present a robust optimization approach to balance thoseconcerns and extend our framework to dynamic policies basedon information revealed over time about competitors’ products
entering the market.

Thu.3 13:30–14:45

PDE
Thu.3 H 2013
PDE-constrained
Optimization
Under
Uncertainty(Part III)
Organizers: Harbir Antil, Drew Philip Kouri, Thomas Michael
Surowiec, Michael Ulbrich, Stefan Ulbrich
Chair: Michael Ulbrich

171

172

Parallel Sessions Abstracts Thu.3 13:30–14:45

ROB
Thu.3 H 3006
Dynamic Optimization Under Data Uncertainty
Organizer: Omid Nohadani
Chair: Omid Nohadani
Sebastian Pokutta, Georgia Tech (joint work with AndreasBärmann, Alexander Martin, O. Schneider)
From Robust Optimization to Online Inverse Optimization
Robust optimization can be solved via online learning requiring only access to the nominal problem for a given uncertainty
realization. We show that a similar perspective can be assumed
for an online learning approach to online inverse optimization, even for discrete and non-convex decisions. The decision
maker observes an expert’s decisions over time and learns an
objective function that is equivalent to the expert’s private
objective. We present a new framework for inverse optimization
through online learning that goes beyond duality approaches
and demonstrate its real-world applicability.
Eojin Han, Northwestern University (joint work with
Chaithanya Bandi, Omid Nohadani)
Robust Periodic-Affine Policies for Multiperiod Dynamic
Problems
We introduce a new class of adaptive policies called periodicaffine policies, that allows a decision maker to optimally manage
and control large-scale newsvendor networks in the presence of
uncertain demand without distributional assumptions. These
policies model many features of the demand such as correlation, and can be generalized to multi-product settings andmulti-period problems. This is accomplished by modeling theuncertain demand via sets. We provide efficient algorithms anddemonstrate their advantages on the sales data from one ofIndia’s largest pharmacy retailers.
Dan Iancu, Stanford University (joint work with Nikos Trichakis,
Do-Young Yoon)
Monitoring With Limited Information
We consider a system with an evolving state that can be
stopped at any time by a decision maker (DM), yielding astate-dependent reward. The DM observes the state only at
limited number of monitoring times, which he must choose, in
conjunction with a stopping policy. We propose a robust optimization approach, whereby adaptive uncertainty sets capture
the information acquired through monitoring. We consider two
versions of the problem, static and dynamic and show that,
under certain conditions, the same reward is achievable undereither static or dynamic monitoring.

SPA
Thu.3 H 1012
Sparse Optimization and Information Processing - Contributed Session III
Organizer: Shoham Sabach
Chair: Bogdan Toader
Ryan Cory-Wright, Massachusetts Institute of Technology(joint work with Dimitris Bertsimas, Jean Pauphilet)
A Dual Perspective on Discrete Optimization: Nonlinear
Reformulations and Scalable Algorithms
This talk provides a framework for deriving tractable nonlinear reformulations of semi-continuous optimization problems.
By imposing ridge or big-M penalty terms, we invoke strong
duality to reformulate semi-continuous problems as tractable
mixed-integer convex optimization problems. We also study
the reformulation’s continuous relaxation, provide a sufficientcondition for tightness, and propose a randomized rounding
scheme which provides high-quality warm-starts. Finally, we
present promising computational results for facility location,
network design and unit commitment problems.
Bolin Pan, UCL (joint work with Simon Arridge, Paul Beard,
Marta Betcke, Ben Cox, Nam Huynh, Felix Lucka, Edward
Zhang)
Dynamic Photoacoustic Reconstruction using Curvelet
Sparsity with Optical Flow Constraint
In photoacoustic tomography, the acoustic propagation time
across the specimen is the ultimate limit on sequential sampling frequency. In this talk, we first consider the photoacoustic
reconstruction problem from compressed/subsampled measurements by utilizing sparsity in the Curvelet frame. Then we
introduce the dynamic photoacoustic reconstruction from subsampled/compressed dynamic data using Curvelet sparsity inthe photoacoustic image domain. Additionally, spatio-temporal
regularization via optical flow constraint is applied.
Bogdan Toader, Oxford University (joint work with Stephane
Chretien, Andrew Thompson)
The dual approach to non-negative super-resolution: impact on primal reconstruction accuracy
We study the problem of super-resolution using TV norm
minimisation, where we recover the locations and weights of
non-negative point sources from a few samples of their convolution with a Gaussian kernel. A practical approach is to solve
the dual problem. In this talk, we study the stability of solutions
with respect to the solutions to the dual problem. In particular,
we establish a relationship between perturbations in the dual
variable and the primal variables around the optimiser. Thisis achieved by applying a quantitative version of the implicit
function theorem in a non-trivial way.

Thu.3 13:30–14:45

Parallel Sessions Abstracts Thu.3 13:30–14:45

STO
Thu.3 H 3012
Solution and Analysis of Stochastic Programming andStochastic Equilibria
Chair: Shu Lu
Pedro Borges, IMPA (joint work with Claudia Sagastizábal,
Mikhail Solodov)
New Techniques for Sensitivity Analysis of Solution Mappings with Applications to Possibly Non-Convex Stochastic Programming
We introduce a well-behaved regularization of solution mappings of parametric convex problems that is single-valued andsmooth. We do not assume strict complementarity or second order sufficiency. Such regularization gives a cheap uppersmoothing for possibly non-convex optimal value functions.
Classical first and second order derivatives of the regularized
solution mappings can be obtained cheaply solving certain
linear systems. For convex two-stage stochastic problems theinterest of our techniques is assessed, comparing its numericalperformance against a bundle method.
Shu Lu, University of North Carolina at Chapel Hill
Statistical inference for piecewise normal distributions andapplication to stochastic variational inequalities
Confidence intervals for the mean of a normal distribution
with a known covariance matrix can be computed using closedform formulas. In this talk, we consider a distribution that is theimage of a normal distribution under a piecewise linear function,and provide a formula for computing confidence intervals forthe mean of that distribution given a sample under certain
conditions. We then apply this method to compute confidence
intervals for the true solution of a stochastic variational inequality. This method is based on a closed-form formula whichmakes computation very efficient.

173

Index of Authors

175

Index of Authors
bold : presenter (with starting time)italic : co-author[c] : session chair[o] : session organiser
Abramson, Mark, Utah Valley University
Tue.4, H 2038 100 [16:30]
Adam, Lukáš [canceled], Southern University of Science andTechnology
Thu.1, H 1058 152 [9:00]
Aderinto, Yidiat Omolade, University of Ilorin
Thu.3, H 3004 164 [13:55]
Agrawal, Akshay, Stanford University
Tue.2, H 2032 75 [14:05]
Tue.4, H 0111 102
Ahmadi, Amir Ali, Princeton University
Wed.3, H 2032 134[c], 134[o], 134 [16:50]
Ahookhosh, Masoud, KU Leuven
Wed.3, H 3025 135 [16:00]
Al-Baali, Mehiddin, Sultan Qaboos University
Wed.2, H 1029 125[c], 125 [15:05]
Wed.3, H 1029 137[c]Alacaoglu, Ahmet, EPFL
Wed.3, H 0104 131 [16:50]
Alhaddad, Waleed, King Abdullah University of Science andTechnology (KAUST)
Poster Session 22
Ali, Montaz, WITS
Tue.4, H 2033 105 [17:20]
Alphonse, Amal, Weierstrass Institute for Applied Analysis
and Stochastics (WIAS)
Mon, 16:20, Best Paper Session, H 0105 13
Tue.3, H 0110 86[c], 86[o]Wed.2, H 3013 121[c], 121[o]Ang, Man Shun, UMONS
Wed.3, H 3007 138 [16:50]
Antil, Harbir, George Mason University
Tue.4, H 0112 103[c], 103[o]Wed.1, H 0112 116[o], 116
Thu.1, H 2013 151[o], 151 [9:25]
Thu.2, H 2013 162[c], 162[o]Thu.3, H 2013 171[o]Antonakopoulos, Kimon, University Grenoble Alpes/Inria
Wed.2, H 2038 129 [15:05]
Aragón Artacho, Francisco Javier, University of AlicanteThu.1, H 1028 147 [9:00]
Thu.2, H 1028 158[c], 158[o]Araya, Yousuke, Akita Prefectural University
Wed.1, H 3002 112 [11:30]
Arnaout, Jean-Paul, Gulf University for Science & Technology
Thu.3, H 3004 164[c], 164 [14:20]
Arndt, Rafael, Humboldt-Universität zu Berlin
Tue.3, H 0110 86 [15:35]
Aronna, Maria Soledad, Fundação Getúlio Vargas
Tue.2, H 0107 81
Tue.4, H 0107 103 [16:30]
Asi, Hilal, Stanford University
Tue.3, H 0104 90 [15:10]
Atenas, Felipe, University of Chile
Tue.2, H 1012 73 [13:15]
Auger, Anne, Inria
Tue.3, H 2038 88[c]Wed.1, H 2033 112 [11:30]
Aybat, N. Serhat, Penn State University
Thu.1, H 0104 149 [9:50]
Aydilek, Asiye, Gulf University for Science and Technology
Tue.4, H 0111 102
Thu.2, H 3004 154 [11:10]

Aydilek, Harun, Gulf University for Science and Technology
Tue.4, H 0111 102 [17:20]
Thu.2, H 3004 154
Badenbroek, Riley, Tilburg University
Tue.3, H 3006 87 [15:10]
Bai, Yuanlu, Columbia University
Wed.1, H 3006 117 [12:20]
Bandi, Chaithanya, Kellogg School of Management, Northwestern University
Tue.4, H 2053 104 [16:30]
Thu.3, H 3006 172
Banholzer, Stefan, Universität Konstanz
Wed.1, H 3002 112 [12:20]
Barakat, Anas, LTCI, Télécom Paris, Institut Polytechniquede Paris
Poster Session 22
Barker, Matt, Imperial College London
Mon.2, H 3007 52 [14:10]
Barlaud, Michel, University of Nice Sophia Antipolis
Wed.2, H 2013 126 [14:40]
Bassily, Raef, The Ohio State University
Wed.3, H 0110 132 [16:50]
Bauschke, Heinz, University of British Columbia
Wed, 17:30, Semi-Plenary Talk, H 0105 17
Bayer, Christian, WIAS
Wed.3, H 3012 142 [16:00]
Thu.3, H 2053 170
Becerril, Jorge, Universidade do Porto
Tue.4, H 0107 103 [16:55]
Beck, Amir, Tel Aviv University
Mon, 9:30, Plenary Talk, H 0105 12
Becker, Jan, TU Darmstadt
Mon.1, H 3007 41 [11:00]
Becker, Stephen, University of Colorado Boulder
Tue.2, H 1029 76 [13:15]
Bednarczuk, Ewa, Warsaw University of Technology
Wed.3, H 2038 141
Wed.3, H 3025 135 [16:50]
Belkin, Mikhail, Ohio State University
Tue.1, H 2013 62 [11:20]
Wed.3, H 0110 132
Benko, Matúš, Johannes Kepler University Linz
Tue.2, H 1028 76[c], 76[o], 76 [13:40], 76
Benning, Martin, Queen Mary University of London
Tue.3, H 3007 94[c], 94[o], 94, 94 [15:35]
Thu.2, H 1012 163[o]Berahas, Albert, Lehigh University
Mon.1, H 0105 45[c], 45[o]Mon.2, H 0105 55[c], 55[o]Tue.1, H 0105 67[c], 67[o]Tue.2, H 0105 79[c], 79[o], 79 [14:05]
Tue.3, H 0105 85[c]Tue.4, H 2013 97[c], 97[o], 97
Wed.2, H 0104 120[o]Wed.2, H 2033 123
Wed.3, H 0104 131[c], 131[o]Berggren, Martin, Umeå Universitet
Tue.4, H 0106 102 [16:30]
Berglund, Erik, KTH Royal University of Technology
Wed.3, H 1029 137 [16:00]
Bergmann, Ronny, TU Chemnitz
Tue.3, H 0106 92
Thu.1, H 0112 151 [9:00]
Thu.2, H 1029 159
Bergou, El houcine, INRA-KAUST
Wed.1, H 2013 114[o], 114, 114 [11:55]
Wed.2, H 2013 126[c], 126[o]Wed.3, H 2013 138[c]176

Index of Authors

Besançon, Mathieu, INRIA Lille Nord-Europe
Wed.2, H 3007 129 [14:40]
Bettiol, Enrico, Université Paris 13
Thu.3, H 2038 166[c], 166 [14:20]
Bettiol, Piernicola, Université de Bretagne Occidentale
Wed.1, H 0111 116 [12:20]
Betz, Livia, Universität Duisburg-Essen
Wed.2, H 0106 127 [15:05]
Biehl, Johanna Katharina, TU Darmstadt
Tue.3, H 0112 93 [15:10]
Bigi, Giancarlo, Università di Pisa
Mon.1, H 3007 41 [11:25]
Birgin, Ernesto G., Universidade de São Paulo
Wed.1, H 1012 113 [12:20]
Blanchet, Jose, Stanford University
Tue.3, H 3012 94
Tue.4, H 1058 104 [16:30]
Blekherman, Grigoriy, Georgia Institute of Technology
Wed.1, H 2032 110[c], 110[o], 110 [12:20]
Wed.2, H 2032 122[c], 122[o]Bluhm, Andreas, Technical University of Munich
Mon.1, H 2053 42 [11:25]
Bonart, Henning, Technische Universität Berlin
Mon.2, H 2033 59 [14:35]
Borges, Pedro, IMPA
Thu.3, H 3012 173 [13:30]
Boţ, Radu, University of Vienna
Wed, 10:15, Semi-Plenary Talk, H 0104 16
Wed.3, H 1028 134
Boukouvala, Fani, Georgia Institute of Technology
Wed.3, H 2033 135 [16:00]
Boumal, Nicolas, Princeton University
Wed.1, H 0105 113[o], 113 [12:20]
Wed.2, H 0105 124[o]Boumaza, Kenza, INRA
Mon.2, H 3012 57 [13:45]
Brezhneva, Olga, Miami University
Thu.1, H 0106 149
Thu.2, H 0106 160 [10:45]
Briceño-Arias, Luis, Universidad Tecnica Federico Santa Maria
Tue.2, H 0112 82 [13:40]
Wed.3, H 2038 141
Brokate, Martin, TU München
Wed.1, H 3013 109 [11:30]
Brosch, Daniel, Tilburg University
Wed.1, H 0107 110 [12:20]
Brüggemann, Jo Andrea, Weierstrass Institute for Applied
Analysis and Stochastics (WIAS)
Wed.2, H 3013 121 [14:40]
Poster Session 22
Brune, Alexander, University of Birmingham
Wed.1, H 3004 107 [11:55]
Brust, Johannes, Argonne National Laboratory
Wed.2, H 1029 125 [14:15]
Burke, James, University of Washington
Mon.1, H 1028 43 [11:25]
Tue, 16:30, Memorial Session, H 0105 15
Caballero, Renzo, King Abdullah University of science andtechnology (KAUST)
Poster Session 22
Cai, Xingju, Nanjing Normal University
Thu.3, H 0104 168 [13:30]
Cannarsa, Piermarco, University of Rome Tor Vergata
Thu, 15:15, Semi-Plenary Talk, H 0104 18
Cao, Liyuan, Lehigh University
Wed.2, H 2033 123 [15:05]
Cao, Tan [canceled], State University of New York–Korea
Tue.3, H 0107 92 [15:10]

Thu.3, H 2053 170
Cartis, Coralia, Oxford University
Mon.1, H 2038 44 [11:00]
Tue.1, H 3002 68, 68
Tue.2, H 3025 80[o]Wed.1, H 1012 113
Wed.1, H 1029 114[o]Wed.2, H 2033 123
Wed.2, H 2053 126
Wed.3, H 0105 136[c], 136[o]Caruso, Francesco, University of Naples ”Federico II”
Thu.1, H 3013 145 [9:00]
Celledoni, Elena, Norwegian University of Science and Technology
Tue.3, H 3007 94 [14:45]
Červinka, Michal, Czech Academy of Sciences
Tue.2, H 1028 76 [14:05]
Chandrasekaran, Venkat, California Institute of Technology(Caltech)Tue.1, H 2032 64[o], 64 [10:55]
Tue.2, H 2032 75[c], 75[o], 75
Chang, Tsung-Hui, The Chinese University of Hong Kong,
ShenzhenWed.1, H 0104 108 [11:55]
Chang, Tyler, Virginia Polytechnic Institute and State University (Virginia Tech)
Thu.2, H 3025 159 [10:45]
Chelmus, Teodor, Alexandru Ioan Cuza University of Iasi
Tue.4, H 3013 101 [16:55]
Chen, Caihua, Nanjing University
Thu.3, H 0104 168 [13:55]
Chen, De-Han, Central China Normal University
Mon.2, H 2013 58 [14:10]
Chen, Jongchen, National Yunlin University of Science andTechnology
Poster Session 22
Chen, Po-An, National Chiao Tung University
Thu.2, H 3004 154[c], 154 [11:35]
Poster Session 23
Chen, Xiaojun, Hong Kong Polytechnic University
Wed.1, H 3012 118 [11:30]
Chen, Yadan, Academy of Mathematics and System Science,
Chinese Academy of Sciences
Tue.4, H 3007 106 [16:55]
Chen, Yuxin, Princeton University
Mon, 16:20, Best Paper Session, H 0105 13
Thu.2, H 0105 155 [10:45]
Chen, Zhi, City University of Hong Kong
Wed.3, H 3006 141
Thu.2, H 3006 162 [11:35]
Chen, Zhiping, Xi’an Jiaotong University
Tue.4, H 3012 105 [17:20]
Chenche, Sarah, Guelma University Algeria
Poster Session 22
Cheng, Jianqiang [canceled], University of Arizona
Tue.3, H 1058 93 [14:45]
Chi, Yuejie, Carnegie Mellon University
Wed.3, H 0110 132 [16:00]
Chlumsky-Harttmann, Fabian, TU KaiserslauternThu.1, H 0111 148 [9:00]
Chouzenoux, Emilie, LIGM, Université Paris-Est Marne-laVallée (UMR CNRS 8049)
Wed.3, H 2038 141[o], 141
Chretien, Stephane [canceled], National Physical Laboratory
Thu.3, H 0105 164 [13:30]
Thu.3, H 1012 172
Christof, Constantin, Technical University of Munich
Tue.2, H 0107 81 [13:40]

Index of Authors

Wed.1, H 0106 115[o]Wed.2, H 0106 127[c], 127[o]Wed.3, H 0106 139[o], 139, 139
Clason, Christian, Universität Duisburg-Essen
Mon.1, H 3006 46 [11:00]
Tue.1, H 1029 65
Wed.1, H 0106 115[c], 115[o]Wed.2, H 0106 127[o], 127
Wed.3, H 0106 139[c], 139[o]Coey, Chris, MIT OR Center
Mon.1, H 0111 41 [11:50]
Mon.2, H 0111 52
Combettes, Patrick L., North Carolina State University
Tue.3, H 1028 87[c], 87[o]Wed.1, H 1028 111[c], 111[o], 111 [12:20]
Wed.2, H 1028 122[c], 122[o], 122
Wed.3, H 1028 134[c], 134[o]Cortez, Karla, Faculdade de Engenharia da Universidade do
Porto
Tue.1, H 0107 69 [10:55]
Cory-Wright, Ryan, Massachusetts Institute of Technology
Thu.3, H 1012 172 [13:30]
Cristofari, Andrea, Università di Padova
Tue.2, H 2038 77
Thu.3, H 2032 169 [13:55]
Csetnek, Robert Ernö, University of Vienna
Wed.2, H 2038 129 [14:15]
Wed.3, H 1028 134
Csizmadia, Zsolt, FICO
Tue.3, H 2053 91 [15:10]
Cui, Ying, University of Southern California
Mon.1, H 0104 39[o], 39
Tue.2, H 2013 73[o]Curtis, Frank E., Lehigh University
Mon.1, H 0105 45 [11:50]
Tue.2, H 0105 79
Wed.1, H 1029 114
Thu, 15:15, Semi-Plenary Talk, H 0105 18
Custodio, Ana Luisa, Universidade Nova de Lisboa
Mon.1, H 2038 44[o]Mon.2, H 2038 54[o]Tue.1, H 2038 66[o]Tue.2, H 2038 77[o]Tue.3, H 2038 88[o]Tue.4, H 2038 100[o]Wed.1, H 2033 112[o], 112 [12:20]
Wed.2, H 2033 123[o]Wed.3, H 2033 135[o]Thu.1, H 3025 147[c], 147[o]Thu.3, H 3025 ??[c]D’Ambrosio, Claudia, CNRS / École PolytechniqueTue.4, H 1012 101 [16:55], 101d’Aspremont, Alexandre, CNRS / École Normale Supérieure
Tue.3, H 0104 90
Tue.3, H 0105 85 [14:45]
da Cruz Neto, João Xavier, Federal University of Piauı́
Thu.1, H 0107 150 [9:00], 150
Dahito, Marie-Ange, PSA Group / École PolytechniqueMon.2, H 2032 56 [14:10]
Dahl, Joachim, Joachim Dahl
Thu.2, H 2032 157[c], 157 [10:45]
Dai, Yu-Hong, Chinese Academy of Sciences
Tue.1, H 3025 68[c], 68[o], 68, 68 [11:20]
Thu.3, H 0104 168[c], 168[o]Dan, Hiroshige, Kansai University
Wed.1, H 1029 114 [11:30]
Darehmiraki, Majid, Behbahan Khatam Alanbia University ofTechnology

177

Thu.1, H 1029 148 [9:00]
Poster Session 22
Darvay, Zsolt, Babes-Bolyai University
Mon.2, H 3002 51 [13:45]
Davis, Damek, Cornell University
Mon, 16:20, Best Paper Session, H 0105 14
Davoli, Elisa, University of Vienna
Mon.2, H 0106 58 [13:45]
de Carvalho Bento, Gladyston, TU Chemnitz
Thu.1, H 0107 150
Thu.2, H 0112 161 [11:35]
de Klerk, Etienne, Tilburg University
Tue.3, H 3006 87[c], 87[o], 87
Wed.1, H 0107 110
Wed.2, H 2032 122 [14:15]
Thu.1, H 2033 146[o]Thu.1, H 2038 146[o]Thu.2, H 2033 157[o]Thu.2, H 2038 158[o]Thu.3, H 2033 166[o]Thu.3, H 2038 166[o]De los Reyes, Juan Carlos, Escuela Politécnica Nacional
Mon.1, H 0106 47 [11:00]
Mon.2, H 1028 53
De Marchi, Alberto, Bundeswehr University Munich
Mon.2, H 3013 55[c], 55 [14:35]
de Ruiter, Frans, Tilburg University, The Netherlands
Wed.3, H 3004 140[c], 140[o], 140 [16:50]
De Santis, Marianna, Sapienza University of Rome
Tue.3, H 2032 86 [14:45]
de Wolff, Timo, TU Braunschweig
Tue.1, H 2032 64
Tue.4, H 2032 98 [17:20]
Wed.1, H 2032 110
Debroux, Noémie, University of Cambridge
Thu.2, H 1012 163 [11:10]
Defazio, Aaron [canceled], Facebook
Wed.2, H 0104 120 [14:40]
Demenkov, Maxim, Institute of Control Sciences
Tue.2, H 0111 80 [13:15]
Poster Session 22
den Hertog, Dick, Tilburg University, The Netherlands
Mon.1, H 1058 48 [11:25]
Mon.2, H 3025 60, 60
Wed.3, H 3004 140
Dentcheva, Darinka, Stevens Institute of Technology
Mon, 15:30, Semi-Plenary Talk, H 0105 12
Desilles, Anna, ENSTA ParisTech
Mon.1, H 3012 46 [11:00]
Diago, Miguel, Polytechnique Montréal
Mon.2, H 2038 54 [14:10]
Diamond, Steven, Stanford University
Tue.2, H 2032 75
Tue.4, H 0111 102 [16:30]
Dikariev, Ilya, University BTU Cottbus-Senftenberg
Mon.2, H 3012 57 [14:10]
Dinc Yalcin, Gulcin, Eskisehir Technical University
Thu.3, H 0106 168 [13:30], 168
Ding, Chao, AMSS, Chinese Academy of Sciences
Tue.3, H 1029 88[c], 88[o], 88, 88 [15:35]
Ding, Lijun [canceled], Cornell University
Thu.1, H 2038 146 [9:50]
Dixit, Avinash, Indian Institute of Technology (BHU) Varanasi
Wed.3, H 3025 135 [16:25]
Doikov, Nikita, UCLouvain
Mon.2, H 0105 55 [14:35]
Dong, Guozhi, Humboldt-Universität zu Berlin
Thu.1, H 3007 144[c], 144 [9:50]

178

Index of Authors

Dong, Hongbo [canceled], Washington State University
Tue.3, H 3002 89 [15:10]
Dong, Zhi-Long, Xi’an Jiaotong University
Tue.1, H 3025 68 [10:30]
Dorostkar, Ali, Isfahan Mathematics House (IMH)
Wed.1, H 1058 111 [11:55]
Dragomir, Radu-Alexandru, Université Toulouse Capitole &D.I. Ecole Normale SupérieureThu.1, H 1012 152 [9:00]
Driggs, Derek, University of Cambridge
Wed.2, H 2053 126 [14:15]
Drori, Yoel, Google
Tue.4, H 3002 100 [16:30]
Du, Yu, University of Colorado DenverTue.3, H 1028 87 [14:45]
Duchi, John, Stanford University
Tue.3, H 0104 90
Tue.4, H 1058 104 [16:55]
Wed.1, H 1012 113
Dunn, Anthony, University of Southampton
Thu.2, H 3013 156 [10:45]
Durea, Marius, Alexandru Ioan Cuza University of Iasi
Tue.2, H 3013 78 [14:05]
Tue.4, H 3013 101, 101
Dutta, Aritra, King Abdullah University of Science and Technology (KAUST)
Wed.1, H 2013 114[c], 114[o]Wed.2, H 2013 126[o], 126 [14:15]
Dvinskikh, Darina, Weierstrass Institute for Applied Analysis
and Stochastics (WIAS)
Wed.2, H 2038 129 [14:40]
Wed.3, H 3002 132
Dvurechensky, Pavel, Weierstrass Institute for Applied Analysis and Stochastics (WIAS)
Mon.2, H 1028 53
Tue.2, H 2033 84 [13:40]
Wed.3, H 3002 132[o], 132
Thu.1, H 3025 147
E.-Nagy, Marianna, Budapest University of Technology andEconomics
Mon.1, H 3002 40 [11:00]
Ebeling-Rump, Moritz, Weierstrass Institute for Applied Analysis and Stochastics (WIAS)
Tue.1, H 0112 70 [10:55]
Eberhard, Andrew, RMIT University
Thu.3, H 1029 167[c], 167 [14:20]
Eckstein, Jonathan, Rutgers University
Tue.3, H 1028 87[o], 87, 87 [15:35]
Wed, 17:30, Semi-Plenary Talk, H 0105 17[c]Eftekhari, Armin, EPFL
Tue.1, H 3007 71 [10:30]
Eichfelder, Gabriele, TU Ilmenau
Mon.1, H 3013 44[o]Mon.2, H 3013 55[o]Wed.1, H 3002 112[o]Thu.1, H 0111 148[o]Ek, David, KTH Royal Institute of Technology
Mon.1, H 2032 45 [11:25]
Ekici, Ali, Ozyegin University
Thu.1, H 3004 143[c], 143 [9:50]
El Housni, Omar, Columbia University
Tue.1, H 3012 71 [10:55]
Tue.4, H 2053 104
El-Bakry, Amr S., ExxonMobil Upstream Research Comp
Mon, 15:30, Semi-Plenary Talk, H 0104 13
Eltved, Anders, Technical University of DenmarkThu.2, H 2033 157[c], 157 [11:35]
Engel, Sebastian, Technische Universität München

Wed.1, H 0106 115 [12:20]
Engelmann, Alexander, Karlsruhe Institute of Technology
Wed.3, H 0112 140 [16:25]
Erdogdu, Murat, University of Toronto
Tue.1, H 2053 67 [10:55], 67
Espath, Luis, King Abdullah University of science and technology (KAUST)
Wed.2, H 3012 130 [15:05]
Evkaya, Ozan, Atilim University
Thu.1, H 1029 148[c], 148 [9:50]
Fält, Mattias, Lund University
Tue.2, H 0111 80 [13:40]
Fang, Ethan, The Pennsylvania State University
Thu.2, H 0105 155[c], 155[o]Thu.3, H 0105 164[c], 164[o]Fang, Kun, University of Cambridge
Mon.1, H 2053 42 [11:50]
Fanzon, Silvio, University of Graz
Thu.3, H 0112 170 [13:55]
Fawzi, Hamza, University of Cambrige
Mon.1, H 2053 42[c], 42[o], 42
Mon.2, H 0112 53 [13:45]
Faybusovich, Leonid, University of Notre Dame
Tue.4, H 3006 99 [16:55]
Feiling, Jan, Porsche AG
Tue.2, H 2038 77 [14:05]
Feldmeier, Michael, The University of Edinburgh
Wed.1, H 1029 114 [11:55]
Fernández, José, University of Murcia
Thu.1, H 1029 148 [9:25]
Ferris, Michael, University of Wisconsin-Madison
Wed.3, H 1058 133[c], 133[o], 133 [16:50]
Thu.1, H 3013 145[c], 145[o]Filipecki, Bartosz, TU Chemnitz
Tue.1, H 1012 62[c], 62 [11:20]
Forets, Marcelo, Universidad de la República
Mon.1, H 0111 41 [11:00]
Forouzandeh, Zahra, Faculdade de Engenharia da Universidade do Porto
Mon.2, H 3013 55 [14:10]
Fountoulakis, Kimon, University of Waterloo
Tue.1, H 3004 63[c], 63[o], 63 [11:20]
Franca, Guilherme, Johns Hopkins University
Wed.2, H 1058 123 [14:15]
Freund, Robert, Massachusetts Institute of Technology
Tue.2, H 3006 75[c], 75[o]Wed.2, H 0107 121 [14:15]
Friedlander, Michael, University of British Columbia
Mon.2, H 2032 56
Wed.2, H 0107 121 [14:40]
Wed.3, H 3008 136
Fukuda, Mituhiro, Tokyo Institute of Technology
Mon.1, H 0112 42[o]Tue.1, H 1029 65
Tue.1, H 3006 64[o], 64
Funke, Simon, Simula Research Laboratory
Tue.2, H 0107 81 [13:15]
Poster Session 22
Gajardo, Pedro, Universidad Tecnica Federico Santa Maria
Mon.1, H 3012 46[c], 46[o], 46 [11:50]
Mon.2, H 3012 57[c], 57[o]Galabova, Ivet, University of Edinburgh
Tue.3, H 0111 91 [15:10]
Gangl, Peter, Graz University of Technology
Tue.1, H 0106 69
Tue.2, H 0106 81 [14:05]
Gao, Rui, University of Texas at Austin
Tue.1, H 3012 71 [11:20]

Index of Authors

Tue.4, H 3012 105[c], 105[o]Gao, Yini, Singapore Management University
Wed.1, H 3007 117 [12:20]
Garmatter, Dominik, TU Chemnitz
Tue.2, H 2053 79 [13:40]
Garrigos, Guillaume, Université Paris Diderot
Wed.1, H 2038 118 [11:30]
Garstka, Michael, Oxford University
Tue.2, H 0111 80[c], 80 [14:05]
Gasnikov, Alexander, Moscow Institute of Physics and Technology
Mon.2, H 1028 53
Tue.2, H 2033 84
Wed.2, H 1058 123
Wed.3, H 3002 132 [16:25]
Thu.1, H 3025 147
Gauger, Nicolas, TU KaiserslauternWed.3, H 0112 140 [16:00]
Gautam, Pankaj, Indian Institute of Technology (BHU)
Varanasi
Wed.1, H 3008 109 [11:55]
Gazagnadou, Nidham, Télécom ParisTech
Tue.3, H 2033 95 [14:45]
Gebken, Bennet, Paderborn University
Mon.1, H 3013 44 [11:25]
Geetha Jayadev, Gopika, University of Texas at Austin
Tue.2, H 1012 73 [13:40]
Georghiou, Angelos, McGill University, Montréal
Mon.2, H 1058 59[c], 59[o], 59, 59 [14:35]
Gfrerer, Helmut, Johannes Kepler University Linz
Tue.4, H 0110 98[c], 98[o], 98, 98 [17:20]
Wed.1, H 3013 109[c], 109[o]Ghadimi, Saeed, Princeton University
Tue.1, H 0104 72
Wed.3, H 2013 138 [16:00]
Ghilli, Daria, University of Padua
Tue.2, H 0112 82 [14:05]
Ghosal, Shubhechyya, Imperial College Business SchoolTue.2, H 3012 83 [13:40]
Gidel, Gauthier, Université de Montréal
Mon.2, H 2053 56 [14:35]
Giselsson, Pontus, Lund University
Tue.2, H 0111 80
Tue.3, H 2033 95
Thu.1, H 1028 147[o]Thu.2, H 1028 158 [11:10], 158
Thu.3, H 1028 167[o]Poster Session 23
Glineur, François, Université catholique de Louvain
Tue.4, H 3002 100[o], 100 [17:20]
Thu.1, H 2033 146
Goez, Julio C [canceled], NHH Norwegian School of Economics
Thu.2, H 2032 157[o], 157 [11:35]
Gomez, Walter, Universidad de La Frontera
Thu.1, H 2038 146[c], 146 [9:25]
Gonçalves, Douglas, UFSC Federal University of Santa Catarina
Mon.2, H 2032 56 [14:35]
Gondzio, Jacek, The University of Edinburgh
Tue.2, H 3025 80
Tue.4, H 0111 102
Wed.1, H 3004 107 [11:30]
Wed.2, H 3004 119
Wed, 17:30, Semi-Plenary Talk, H 0104 17[c]Thu.2, H 2038 158
Gonzaga, Clovis, LACTEC
Tue.3, H 1012 85 [15:10]

179

González Rodrı́guez, Brais, Universidade de Santiago de Compostela
Thu.2, H 2033 157 [10:45]
Gotoh, Jun-ya, Chuo University
Tue.3, H 3002 89
Wed.2, H 1012 125[o], 125 [14:15]
Wed.3, H 1012 137[c], 137[o]Götschel, Sebastian, Zuse Institute Berlin
Wed.2, H 0112 128 [14:15]
Gounaris, Chrysanthos, Carnegie Mellon University
Wed.2, H 3007 129[c], 129[o], 129
Gouveia, João, University of Coimbra
Wed.1, H 2032 110 [11:55]
Gower, Robert, Telecom Paris Tech
Tue.2, H 2033 84
Tue.3, H 2033 95
Wed.3, H 0104 131 [16:25]
Goyal, Vineet, Columbia University
Tue.1, H 3012 71
Tue.4, H 2053 104[c], 104[o], 104 [17:20]
Goyens, Florentin, University of Oxford
Wed.3, H 0105 136 [16:00]
Grad, Sorin-Mihai, Vienna University
Thu.1, H 1028 147 [9:25]
Grapiglia, Geovani, Universidade Federal do ParanáMon.1, H 0105 45[o]Mon.2, H 0105 55[o]Tue.1, H 0105 67[o]Tue.2, H 0105 79[o]Tue.4, H 3007 106 [16:30]
Wed.1, H 1012 113[c], 113[o]Gräßle, Carmen, Universität Hamburg
Mon.2, H 2033 59[c], 59[o]Poster Session 22
Gratton, Serge, INP-IRIT
Tue.1, H 2038 66
Tue.2, H 2038 77
Wed.1, H 2053 115[o], 115 [11:30], 115, 115
Gribling, Sander, CWI, Amsterdam
Mon.1, H 2053 42 [11:00]
Grigas, Paul, University of California, Berkeley
Mon.1, H 3025 48 [11:00]
Tue.3, H 0105 85[o], 85
Thu.1, H 3006 144[c], 144[o]Thu.2, H 0105 155
Grishchenko, Dmitry, Université Grenoble Alpes
Tue.2, H 0104 78 [14:05]
Groetzner, Patrick, Augsburg University
Thu.1, H 0111 148[c], 148 [9:25]
Gu, Yan, Kyoto University
Thu.3, H 1029 167 [13:55]
Guminov, Sergey, IITP, Russian Academy of Sciences
Wed.2, H 1058 123 [15:05]
Gunasekar, Suriya [canceled], Toyota Technological Instituteat Chicago
Mon.2, H 0104 50 [14:10]
Günther, Christian, Martin Luther University HalleWittenberg
Wed.1, H 3002 112[c], 112 [11:55]
Gupta, S. K., IIT Roorkee
Thu.1, H 3007 144
Thu.2, H 0106 160 [11:35]
Gupta, Vishal, USC Marshall School of BusinessTue.2, H 1058 82 [13:15]
Gurbuzbalaban, Mert, Rutgers University
Tue.3, H 0104 90 [14:45]
Gurol, Selime, IRT Saint Exupéry / CERFACS
Wed.1, H 2053 115 [11:55]

180

Index of Authors

Guth, Philipp, University of Mannheim
Thu.1, H 2013 151 [9:50]
Haeser, Gabriel, University of São PauloThu.1, H 0106 149[o], 149
Hager, William, University of Florida
Tue.2, H 0104 78 [13:40]
Hahn, Mirko, Otto-von-Guericke University Magdeburg
Mon.2, H 3006 57 [14:10]
Hall, Georgina, INSEAD
Tue.3, H 2032 86 [15:35]
Hall, Julian, University of Edinburgh
Tue.3, H 0111 91 [14:45]
Hallak, Nadav, Tel Aviv University
Tue.1, H 3007 71[c], 71[o], 71 [11:20]
Han, Deren [canceled],
Tue.1, H 3025 68[o]Thu.3, H 0104 168[o], 168 [14:20]
Han, Eojin, Northwestern University
Thu.3, H 3006 172 [13:55]
Hanasusanto, Grani A., The University of Texas at Austin
Mon.1, H 3025 48
Wed.2, H 3006 128 [15:05]
Hansen, Nikolaus, Inria / Ecole PolytechniqueTue.3, H 2038 88 [15:10]
Wed.1, H 2033 112[c], 112
Hante, Falk, FAU Erlangen-Nürnberg
Mon.1, H 3006 46[o], 46 [11:50]
Mon.2, H 3006 57[c], 57[o]Thu.1, H 2053 150[o]Hanzely, Filip, King Abdullah University of science and technology (KAUST)
Tue.2, H 2033 84[o]Tue.3, H 2033 95[o]Wed.2, H 0104 120
Hardering, Hanne, TU Dresden
Thu.3, H 0112 170 [14:20]
Haskell, William, Purdue University
Wed.3, H 3006 141[c], 141[o], 141 [16:50]
Haubner, Johannes,
Tue.1, H 0106 69 [10:55]
Hauswirth, Adrian, ETH Zürich
Thu.3, H 2053 170 [13:55]
Hautecoeur, Cécile, UCLouvain
Thu.1, H 2033 146 [9:00]
Hazimeh, Hussein [canceled], MIT
Thu.1, H 3006 144 [9:25]
He, Niao, University of Illinois at Urbana-Champaign
Wed.2, H 0110 120 [14:40]
Hebestreit,
Niklas, Martin-Luther-University
HalleWittenberg
Tue.1, H 3013 66 [10:30]
Heinkenschloss, Matthias, Rice University
Tue.1, H 0112 70 [10:30]
Wed.2, H 0112 128[o]Wed.3, H 0112 140[c], 140[o]Thu, 16:15, Plenary Talk, H 0105 19[c]Held, Harald, Siemens, Corporate Technology
Tue.4, H 1028 96 [16:55]
Helmberg, Christoph, TU Chemnitz
Tue.1, H 1012 62
Tue.3, H 2032 86 [15:10]
Thu.1, H 2032 145[c], 145[o]Henrion, René, Weierstrass Institute for Applied Analysis andStochastics (WIAS)
Mon, 15:30, Semi-Plenary Talk, H 0105 12[c]Thu.1, H 2013 151 [9:00]
Thu.1, H 3012 153

Hermosilla, Cristopher, Universidad Tecnica Federico Santa
Maria
Tue.1, H 0107 69[c], 69[o]Tue.2, H 0112 82[c], 82[o]Tue.3, H 0107 92[c], 92[o]Tue.4, H 0107 103[c], 103[o]Wed.1, H 0111 116[o]Wed.2, H 0111 127[o]Wed.3, H 0107 139[o], 139 [16:25]
Hertlein, Lukas, Technische Universität München
Wed.1, H 0106 115 [11:55]
Herzog, Roland, TU Chemnitz
Tue.3, H 0106 92
Thu.1, H 0112 151[c], 151[o], 151
Thu.2, H 0112 161[o]Thu.2, H 1029 159
Thu.3, H 0112 170[c], 170[o]Hildebrand, Roland, Laboratoire Jean Kuntzmann / CNRS
Wed.3, H 0111 133[c], 133[o], 133 [16:50]
Hinder, Oliver, Stanford Univ.
Wed.1, H 1012 113 [11:30]
Hintermüller, Michael, Weierstrass Institute for Applied Analysis and Stochastics
Mon.1, H 0106 47[o], 47 [11:25]
Mon.2, H 0106 58[c], 58[o]Mon, 16:20, Best Paper Session, H 0105 13
Tue.3, H 0110 86
Wed.2, H 3004 119
Wed.2, H 3013 121
Thu.1, H 3007 144
Ho-Nguyen, Nam,
Mon.2, H 1029 54 [13:45]
Hoheisel, Tim, McGill University, Montréal
Mon.1, H 1028 43[c], 43[o], 43 [11:50]
Mon.2, H 1028 53[c], 53[o]Tue.2, H 1028 76
Holler, Gernot, University of Graz
Mon.2, H 0106 58 [14:10]
Holley, Jonas, Robert Bosch GmbH
Wed.2, H 3004 119 [15:05]
Hömberg, Dietmar, Weierstrass InstituteTue.1, H 0112 70 [11:20]
Homem-de-Mello, Tito, Universidad Adolfo Ibanez
Thu.3, H 1058 171 [13:55]
Hong, Mingyi, University of Minnesota
Mon.1, H 3004 40[c], 40[o], 40
Thu.1, H 0105 143 [9:25]
Horn, Benjamin Manfred, Technische Universität Darmstadt
Wed.1, H 0112 116 [11:55]
Horvath, Samuel, King Abdullah University of Science andTechnology
Tue.2, H 2033 84[c], 84[o]Tue.3, H 2033 95[c], 95[o], 95 [15:35]
Hosseini Nejad, Seyedeh Masoumeh,
Thu.1, H 1029 148
Poster Session 22
Hrga, Timotej, University of Ljubljana
Thu.3, H 2038 166 [13:55]
Hu, Hao, Tilburg University
Wed.1, H 0107 110 [11:30]
Huber, Olivier, Humboldt-Universität zu Berlin
Wed.3, H 1058 133 [16:00]
Hunter, Susan, Purdue University
Thu.1, H 3025 147 [9:00]
Iancu, Dan, Stanford University
Thu.3, H 3006 172 [14:20]
Illés, Tibor, Budapest University of Technology and Economics,
Budapest, Hungary

Index of Authors

Mon.1, H 3002 40[o], 40 [11:25], 40
Mon.2, H 3002 51[c], 51[o], 51
Iori, Tomoyuki, Kyoto University
Poster Session 23
Ito, Masaru, Nihon University
Tue.1, H 1029 65 [10:30]
Jacobse, Marcel, Universität Bremen
Wed.3, H 1028 134 [16:50]
Jadamba, Baasansuren, Rochester Institute of Technology
Tue.1, H 3013 66 [10:55]
Jaggi, Martin, EPFL
Wed.3, H 0104 131 [16:00]
Jang, Sunho, Seoul National University
Wed.2, H 3012 130 [14:15]
Janzen, Kristina, TU Darmstadt
Tue.2, H 1012 73[c], 73 [14:05]
Jarre, Florian, Heinrich-Heine Universität Düsseldorf
Mon.1, H 2038 44 [11:50]
Jauhiainen, Jyrki, University of Eastern FinlandMon.2, H 0106 58 [14:35]
Javanmard, Adel, University of Southern California
Tue.1, H 2053 67 [10:30]
Jiang, Bo, Shanghai University of Finance and Economics
Mon.2, H 3004 51, 51 [14:35]
Jiang, Bo, Nanjing Normal University
Wed.1, H 3025 107 [12:20]
Jiang, Jie, Xi’an Jiaotong University
Thu.3, H 3013 165 [14:20]
Jiang, Rujun, Fudan University
Mon.2, H 0112 53
Tue.2, H 3002 77 [13:40]
Jiang, Tao, University of Waterloo
Wed.1, H 0104 108 [12:20]
Jost, Felix, Otto-von-Guericke University Magdeburg
Mon.1, H 0107 39[c], 39[o]Mon.2, H 0107 50[o], 50 [14:35]
Kahle, Christian, Technische Universität München
Mon.2, H 2033 59
Tue.3, H 0106 92 [15:35]
Kalise, Dante, University of Nottingham
Tue.1, H 0107 69 [11:20]
Tue.2, H 0112 82
Kamoutsi, Angeliki, ETH Zürich
Tue.1, H 0104 72 [11:20]
Kamzolov, Dmitry, Moscow Institute of Physics and Technology, MIPT
Mon.2, H 1028 53 [13:45]
Kanoh, Shinichi, University of Tsukuba
Mon.1, H 0112 42 [11:25]
Kanzow, Christian, University of Würzburg
Tue.2, H 1028 76
Tue.3, H 0110 86 [14:45]
Kaouri, Maha H., University of Reading
Tue.1, H 3002 68[c], 68 [10:55]
Kapelevich, Lea, MIT Operations Research Center
Mon.1, H 0111 41
Mon.2, H 0111 52 [13:45]
Kara, Güray, Norwegian University of Science and Technology
Tue.3, H 1012 85 [14:45]
Karaca, Orcun, ETH Zürich
Tue.4, H 1028 96[c], 96 [17:20]
Karimi, Mehdi, University of Waterloo
Tue.3, H 3006 87 [14:45]
Kasimbeyli, Refail, Eskisehir Technical University
Thu.3, H 0106 168[c], 168, 168 [14:20]
Keil, Tobias, WIAS Berlin
Mon.2, H 2033 59[o], 59 [13:45]
Kelis, Oleg, Technion -Israel Instutute of Tecnology

181

Wed.3, H 3004 140 [16:00]
Khalloufi, Fatima Ezzahra, Faculty of science of Agadir, Ibn
Zohr University
Thu.3, H 2038 166 [13:30]
Khan, Akhtar A., Rochester Institute of Technology
Tue.1, H 3013 66[c], 66[o], 66
Tue.2, H 3013 78[o]Tue.3, H 3013 89[o], 89 [15:35]
Tue.4, H 3013 101[o]Khirirat, Sarit, Division of Decision and Control
Thu.2, H 3007 156 [11:10]
Kilinc-Karzan, Fatma, Carnegie Mellon University
Mon.1, H 3025 48[c], 48[o]Mon.2, H 1029 54
Tue.4, H 3006 99[c], 99[o], 99 [17:20]
Thu.2, H 2032 157
Kim, Cheolmin, Northwestern University
Thu.3, H 3007 165 [13:55]
Kim, Chesoong, Sangji University
Poster Session 23
Kim, Donghwan, KAIST
Tue.4, H 3002 100 [16:55]
Kim, Sunyoung, Ewha Womans University
Tue.1, H 3006 64 [10:30], 64
Kim, Youngdae, Argonne National Laboratory
Wed.3, H 1058 133 [16:25]
Kimiaei, Morteza, University of Vienna
Tue.1, H 2038 66 [11:20]
Wed.3, H 2033 135 [16:50]
Kirches, Christian, TU Braunschweig
Mon.1, H 3006 46[o]Mon.2, H 3006 57[o], 57
Thu.1, H 2053 150[c], 150[o]Kleinert, Thomas, Friedrich-Alexander-Universität (FAU)
Erlangen-Nürnberg
Thu.3, H 3013 165 [13:30]
Knauer, Matthias, Universität Bremen
Tue.3, H 0107 92 [14:45]
Knees, Dorothee, University of Kassel
Wed.2, H 0106 127 [14:15]
Köbis, Elisabeth, Martin Luther University Halle-Wittenberg
Tue.1, H 3013 66[o], 66
Tue.2, H 3013 78[c], 78[o]Tue.3, H 3013 89[o], 89 [15:10]
Tue.4, H 3013 101[c], 101[o]Poster Session 23, 23
Köbis, Markus Arthur, Free University Berlin
Tue.3, H 3013 89 [14:45]
Poster Session 23, 23
Kočvara, Michal, University of Birmingham
Wed.1, H 3004 107
Wed.2, H 2053 126[c], 126[o]Wed.3, H 3007 138[o], 138 [16:25]
Kocyigit, Cagil, École polytechnique fédérale de LausanneTue.1, H 1058 70 [10:55]
Wed.3, H 3006 141
Kolda, Tamara G., Sandia National Laboratories
Wed, 9:00, Plenary Talk, H 0105 15
Konnov, Igor, Kazan Federal University
Wed.1, H 1058 111 [11:30]
Koppel, Alec, U.S. Army Research Laboratory
Mon.1, H 0110 49[c], 49[o], 49 [11:50]
Mon.2, H 0110 61[c], 61[o]Tue.1, H 0104 72[c], 72[o]Poster Session 23
Kostina, Ekaterina, Heidelberg University
Mon.1, H 0107 39, 39
Thu.2, H 2013 162 [11:10]

182

Index of Authors

Kothari, Pravesh, Carnegie Mellon University
Wed.3, H 2032 134 [16:25]
Kouri, Drew Philip, Sandia National Laboratories
Thu.1, H 2013 151[o]Thu.2, H 2013 162[o], 162
Thu.3, H 2013 171[o]Kovalev, Dmitry, King Abdullah University of Science andTechnology (KAUST)
Mon.2, H 2053 56 [14:10]
Kröner, Axel, Humboldt-Universität zu Berlin
Tue.2, H 0107 81[c], 81[o], 81 [14:05]
Tue.3, H 0112 93[c], 93[o]Krug, Richard, Friedrich-Alexander-Universität ErlangenNürnberg
Mon.1, H 3006 46 [11:25]
Kuhlmann, Renke, University of Wisconsin-Madison
Tue.2, H 3025 80 [13:15]
Kuhn, Daniel, EPFL
Mon.2, H 1058 59
Tue.1, H 1058 70, 70, 70 [11:20]
Tue.3, H 3012 94, 94
Tue.4, H 1058 104
Wed.3, H 3004 140
Wed.3, H 3006 141, 141
Kummer, Mario, TU Berlin
Tue.1, H 0110 63, 63 [11:20]
Kungurtsev, Vyacheslav, Czech Technical University in
PragueWed.1, H 2013 114 [11:30]
Kuryatnikova, Olga, Tilburg University
Tue.4, H 2032 98 [16:30]
Kyng, Rasmus, ETH Zürich
Tue.1, H 3004 63 [10:55]
Lacoste-Julien, Simon, University of Montreal & Mila & SAIL
Montreal
Mon.2, H 2053 56 [13:45]
Laguel, Yassine, Université Grenoble Alpes
Thu.1, H 3012 153 [9:00]
Laird, Carl, Sandia National Laboratories
Wed.2, H 0112 128[c], 128[o]Wed.3, H 0112 140[o], 140 [16:50]
Lam, Henry, Columbia University
Wed.1, H 3006 117[c], 117[o], 117, 117
Lam, Xin-Yee, National University of Singapore
Tue.2, H 3004 74 [13:40]
Lamadrid L., Alberto J., Lehigh University
Tue.3, H 1012 85[c], 85 [15:35]
Lämmel, Sebastian, Technische Universität Chemnitz
Poster Session 23
Lamperski, Jourdain, MIT
Wed.2, H 0107 121
Thu.3, H 2032 169[c], 169 [14:20]
Lan, Guanghui, Georgia Institute of Technology
Mon.2, H 1029 54 [14:10]
Tue.2, H 0104 78[o]Tue.3, H 0104 90[o]Lara, Felipe, Getulio Vargas Foundation
Wed.1, H 3008 109[c], 109 [12:20]
Larson, Jeffrey, Argonne National Laboratory
Mon.1, H 2038 44 [11:25]
Latafat, Puya, KU Leuven
Thu.3, H 1028 167 [13:30]
Laurain, Antoine, Instituto de Matemática e Estatı́sticaMon.2, H 2013 58 [13:45]
Laxmi, Scindhiya, Indian institute of Technology Roorkee
Thu.1, H 3007 144 [9:00]
Le, Hien, University of Mons
Wed.3, H 2013 138 [16:25]

Le Digabel, Sébastien, Polytechnique Montréal
Mon.1, H 2038 44[o]Mon.2, H 2038 54[o], 54 [14:35]
Tue.1, H 2038 66[o]Tue.2, H 2038 77[c], 77[o]Tue.3, H 2038 88[o]Tue.4, H 2038 100[c], 100[o]Wed.1, H 2033 112[o], 112
Wed.2, H 2033 123[o]Wed.3, H 2033 135[o]Thu.1, H 3025 147[o]Lee, Ching-pei, National University of Singapore / Universityof Wisconsin-Madison
Thu.3, H 1029 167 [13:30]
Lee, Chul-Hee, INHA University
Poster Session 23
Lee, Jon, University of Michigan, Ann Arbor
Tue.4, H 1012 101[c], 101[o], 101, 101
Legat, Benoı̂t, UCLouvain
Mon.1, H 0111 41 [11:25]
Lellmann, Jan, University of Lübeck
Thu.2, H 1012 163 [10:45]
Leyffer, Sven, Argonne National Laboratory
Mon.1, H 3006 46[c], 46[o]Mon.2, H 3006 57[o], 57
Tue.3, H 0112 93
Thu.1, H 2053 150[o], 150 [9:50]
Lezcano Casado, Mario, University of Oxford
Wed.3, H 0105 136 [16:50]
Li, Andrew, Carnegie Mellon University
Tue.2, H 1058 82 [13:40]
Li, Chiao-Wei, National Tsing Hua University
Poster Session 23
Li, Fengpei,
Wed.1, H 3006 117 [11:30]
Li, Guoyin, University of New South Wales, Australia
Tue.1, H 1028 65[c], 65[o], 65 [11:20]
Tue.2, H 3002 77
Li, Mingyu, Department of Industrial Economics and Technology Management, NTNU
Thu.1, H 3004 143 [9:25]
Li, Qingna, Beijing Institute of Technology
Wed.1, H 0110 108[c], 108[o], 108 [12:20]
Li, Xiaobo, National University of Singapore
Wed.3, H 3012 142 [16:25]
Li, Xin, University of Central Florida
Wed.1, H 2013 114 [12:20]
Wed.2, H 2013 126
Li, Xinxin, Jilin University
Tue.1, H 3025 68 [10:55]
Li, Xudong, Fudan University
Mon, 16:20, Best Paper Session, H 0105 14
Tue.2, H 2013 73
Tue.2, H 3004 74[c], 74[o]Tue.3, H 1029 88 [15:10]
Tue.4, H 3004 97[c], 97[o]Liang, Jingwei, University of Cambridge
Tue.1, H 1028 65 [10:55]
Liang, Ling, National University of Singapore
Tue.4, H 3004 97 [16:30]
Liang, Tengyuan, University of Chicago
Mon.2, H 0104 50 [14:35]
Liberti, Leo, CNRS / École PolytechniqueTue.4, H 1012 101 [17:20]
Lieder, Felix, Heinrich-Heine-Universität Düsseldorf
Mon.1, H 2038 44
Thu.1, H 2032 145 [9:50]

Index of Authors

Liers, Frauke, Friedrich-Alexander-Universität ErlangenNürnberg
Wed, 10:15, Semi-Plenary Talk, H 0105 16
Lilienthal, Patrick-Marcel, Otto-von-Guericke-Universität
Magdeburg
Mon.2, H 0107 50 [14:10]
Lin, Meixia, National University of Singapore
Tue.4, H 3004 97 [16:55]
Lin, Qihang, University of Iowa
Tue.2, H 1029 76 [13:40]
Wed.2, H 0107 121
Wed.2, H 0110 120[c], 120[o]Wed.3, H 0110 132[o]Thu.1, H 0104 149
Thu.2, H 0104 160
Lin, Tianyi, University of California, Berkeley
Mon.2, H 3004 51
Tue.3, H 2013 90 [14:45]
Lindgren, Jussi, Aalto University School of Science
Wed.2, H 3012 130 [14:40]
Lindstrom, Scott B., The Hong Kong Polytechnic University
Wed.3, H 3008 136 [16:00]
Ling, Qing, Sun Yat-Sen University
Thu.1, H 0105 143 [9:00]
Liu, Heyuan, University of California, Berkeley
Tue.3, H 0105 85 [15:10]
Liu, Huikang, The Chinese University of Hong Kong
Mon.2, H 0112 53 [14:10]
Liu, Sijia, MIT-IBM Watson AI Lab, IBM Research
Mon.1, H 3004 40 [11:25]
Liu, Tianxiang, RIKEN Center for Advanced Intelligence
Project (AIP)
Wed.3, H 1012 137 [16:50]
Liu, Xin, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Mon.2, H 1012 60[c], 60[o], 60
Tue.2, H 3007 83[o], 83 [13:15]
Tue.4, H 3007 106[o], 106
Liu, Ya-Feng, Chinese Academy of Sciences
Wed.1, H 3025 107[o]Wed.2, H 3025 119[c], 119[o], 119 [14:40]
Loayza-Romero, Estefania, Chemnitz University of Technology
Tue.3, H 0106 92 [14:45]
Lobato, Rafael Durbano, Università di Pisa
Thu.3, H 0106 168 [13:55]
Löbhard, Caroline, Weierstrass Institute for Applied Analysis
and Stochastics (WIAS)
Wed.2, H 0111 127 [15:05]
Lobo Pereira, Fernando, Universidade do Porto Faculdade deEngenharia
Wed.3, H 0107 139 [16:50]
Lobos, Alfonso, University of California, Berkeley
Thu.2, H 0105 155 [11:35]
Loizou, Nicolas, The University of Edinburgh
Tue.2, H 2033 84
Tue.4, H 2013 97 [17:20]
Long, Daniel Zhuoyu, CUHK
Tue.4, H 2053 104 [16:55]
Lorenz, Dirk, TU Braunschweig
Mon.1, H 1029 43 [11:00]
Lourenço, Bruno Figueira, University of Tokyo
Tue.1, H 3006 64
Wed.3, H 0111 133
Thu.1, H 0106 149 [9:25]
Thu.2, H 0110 155
Louzeiro, Maurı́cio, UFG
Thu.1, H 0107 150 [9:50]

183

Thu.2, H 2053 161
Lu, Haihao, MIT
Thu.1, H 0104 149 [9:00]
Lu, Kuan, Tokyo Institute of Technology
Wed.2, H 3008 124 [14:15]
Lu, Shu, University of North Carolina at Chapel Hill
Thu.3, H 3012 173[c], 173 [13:55]
Lucchi, Aurelien, ETH Zürich
Wed.2, H 0104 120 [15:05]
Luce, Robert, Gurobi
Tue.2, H 2053 79[o], 79 [14:05]
Tue.3, H 2053 91[c], 91[o]Lucet, Yves, University of British Columbia
Mon.2, H 2038 54 [13:45]
Luke, Russell, Georg-August-University of Göttingen
Mon.1, H 1012 49 [11:00]
Wed, 10:15, Semi-Plenary Talk, H 0104 16[c]Luo, Zhi-Quan, The Chinese University of Hong Kong, Shenzhen
Wed.1, H 3025 107[c], 107[o]Wed.2, H 3025 119[o], 119 [14:15]
Ma, Runchao, University of Iowa
Thu.2, H 0104 160 [11:10]
Ma, Shiqian, University of California, Davis
Mon.2, H 1029 54[o]Wed.1, H 3025 107
Thu.1, H 0104 149[c], 149[o]Thu.2, H 0104 160[o]Magron, Victor, LAAS-CNRS
Wed.2, H 2032 122 [15:05]
Maier, Corinna, University of Potsdam
Mon.2, H 0107 50 [13:45]
Mairal, Julien, Inria
Wed.1, H 2038 118[c], 118[o], 118 [12:20]
Mannel, Florian, University of Graz
Tue.4, H 1029 99 [17:20]
Manns, Paul, TU Braunschweig
Mon.2, H 3006 57 [14:35]
Marandi, Ahmadreza, Eindhoven University of Technology
Mon.2, H 3025 60 [13:45]
Mareček, Jakub, IBM Research
Wed.2, H 2013 126 [15:05]
Marques Alves, Maicon, Federal University of Santa Catarina- UFSC
Tue.3, H 1028 87 [15:10]
Matei, Alexander, TU Darmstadt
Thu.2, H 3025 159 [11:10]
Maxim, Diana, Alexandru Ioan Cuza University of Iasi
Tue.4, H 3013 101 [17:20]
Mazumder, Rahul [canceled], Massachusetts Institute ofTechnology
Thu.1, H 3006 144 [9:00], 144
Mefo Kue, Floriane, University of SiegenMon.2, H 3013 55 [13:45]
Mehlitz, Patrick, Brandenburgische Technische Universität
Cottbus-Senftenberg
Tue.2, H 1028 76 [13:15]
Mehrotra, Sanjay, Northwestern University
Thu.3, H 1058 171 [13:30]
Menhorn, Friedrich, Technical University of Munich
Wed.3, H 2033 135 [16:25]
Menickelly, Matt, Argonne National Laboratory
Mon.1, H 2038 44
Mon.2, H 0105 55
Tue.1, H 2038 66 [10:30]
Merino, Pedro, Escuela Politécnica Nacional
Mon.2, H 1028 53 [14:10]
Mertikopoulos, Panayotis, CNRS

184

Index of Authors

Mon.2, H 3007 52
Tue.2, H 3006 75 [13:40]
Wed.2, H 2038 129
Metel, Michael R., RIKEN AIP
Thu.2, H 0110 155 [11:10]
Meyn, Sean, University of Florida
Mon.1, H 0110 49 [11:00]
Migot, Tangi, University of Guelph
Thu.3, H 3013 165 [13:55]
Mihic, Kresimir, University of Edinburgh
Tue.4, H 0111 102
Thu.2, H 2038 158 [11:10]
Milz, Johannes, Technical University of Munich
Thu.3, H 2013 171 [13:55]
Milzarek, Andre, The Chinese University of Hong Kong, Shenzhen
Mon.1, H 1028 43 [11:00]
Tue.4, H 1029 99[c], 99[o], 99
Mishchenko, Konstantin, KAUST
Mon.2, H 2053 56[c], 56[o]Tue.3, H 2013 90[c], 90[o], 90 [15:35]
Wed.2, H 0104 120
Thu.3, H 0107 169[c], 169[o]Misic, Velibor, UCLA
Tue.2, H 1058 82[c], 82[o], 82 [14:05]
Mitchell, Tim, Max Planck Institute for Dynamics of Complex
Technical Systems
Wed.1, H 1029 114[c], 114 [12:20]
Mohajerin Esfahani, Peyman, TU Delft
Mon.2, H 1058 59
Tue.1, H 1058 70
Tue.3, H 3012 94, 94
Tue.4, H 1058 104[c], 104[o], 104 [17:20]
Thu.2, H 3006 162[c], 162[o]Mohammadi, Masoumeh, TU Darmstadt
Mon.2, H 2033 59 [14:10]
Monteiro, Renato, Georgia Institute of Technology
Tue.2, H 0104 78 [13:15]
Mordukhovich, Boris, Department of Mathematics, Wayne
State University
Tue.1, H 1028 65 [10:30]
Tue.3, H 0107 92
Tue.4, H 0110 98
Thu.3, H 2053 170
Morin, Martin, Lund University
Poster Session 23
Müller, Christian L., Flatiron InstituteWed.2, H 1028 122 [15:05]
Müller, Georg, Universität Konstanz
Wed.3, H 0106 139 [16:50]
Munoz Zuniga, Miguel, IFPEN
Tue.4, H 2038 100 [16:55]
Muramatsu, Masakazu, The University of ElectroCommunications
Mon.1, H 0112 42[o]Tue.1, H 3006 64[o], 64
Wed.3, H 0111 133
Murray, Riley, Caltech
Tue.1, H 2032 64[c], 64[o]Tue.2, H 2032 75[o], 75 [13:40]
Murthy, Karthyek, Singapore University of Technology and
DesignTue.1, H 3012 71[c], 71[o]Tue.3, H 3012 94
Wed.2, H 3006 128 [14:15]
Nakatsukasa, Yuji, University of Oxford
Tue.2, H 2053 79[c], 79[o]Tue.3, H 2053 91[o], 91 [14:45]

Wed.2, H 1012 125[c], 125[o]Wed.3, H 1012 137[o], 137
Nakayama, Shummin, Chuo University
Wed.2, H 1012 125 [14:40]
Naldi, Simone, Université de Limoges
Tue.1, H 0110 63 [10:55]
Nannicini, Giacomo, IBM T.J. Watson
Tue.3, H 2038 88 [15:35]
Natarajan, Karthik, Singapore University of Technology and
DesignTue.3, H 1058 93[c], 93[o], 93, 93 [15:35]
Tue.4, H 3012 105
Wed.2, H 3006 128
Necoara, Ion, University Politehnica Bucharest (UPB)
Mon.1, H 1029 43[o], 43 [11:50]
Mon.2, H 1029 54[c]Tue.1, H 1029 65[o]Tue.2, H 1029 76[c], 76[o]Wed.3, H 0104 131
Nedich, Angelia, Arizona State University
Mon.1, H 1029 43 [11:25]
Nerantzis, Dimitrios, Imperial College London
Thu.2, H 3012 154 [10:45]
Netrapalli, Praneeth, Microsoft Corporation
Wed.3, H 0110 132 [16:25]
Nghia, Tran, Oakland University
Tue.2, H 3002 77 [14:05]
Nguyen, Dang-Khoa, University of Vienna
Wed.3, H 1028 134 [16:25]
Nguyen, Truc-Dao, Wayne State University
Thu.3, H 2053 170 [13:30]
Nguyen, Viet Anh, EPFL
Mon.2, H 1058 59 [14:10]
Tue.1, H 1058 70
Tue.3, H 3012 94
Tue.4, H 1058 104
Nhu, Vu Huu, University of Duisburg-Essen
Wed.2, H 0106 127 [14:40]
Nimana, Nimit, Khon Kaen University
Wed.3, H 1028 134 [16:00]
Ninin, Jordan, ENSTA-Bretagne
Tue.4, H 2033 105[c], 105 [16:55]
Niu, Yi-Shuai, Shanghai Jiao Tong University
Thu.2, H 2033 157 [11:10]
Nocedal, Jorge, Northwestern University
Mon.1, H 0105 45 [11:00]
Thu, 15:15, Semi-Plenary Talk, H 0105 18[c]Nohadani, Omid, Northwestern University
Wed.2, H 3007 129 [15:05]
Thu.3, H 3006 172[c], 172[o], 172
Ochs, Peter, Saarland University
Tue.1, H 3007 71 [10:55]
Okamura, Natsuki, The University of Electro-Communications
Tue.1, H 3006 64 [10:55]
Okuno, Takayuki, RIKEN AIP
Wed.2, H 1012 125 [15:05]
Thu.1, H 2038 146
Oliver, Sophy, University of Oxford
Tue.1, H 3002 68 [10:30]
Olshevsky, Alex, Boston University
Wed.3, H 3002 132 [16:00]
Orban, Dominique, GERAD & PolytechniqueMon.1, H 2032 45[c], 45[o], 45
Mon.2, H 2032 56[o], 56 [13:45], 56
Outrata, Jiri, The Institute of Information Theory and Automation
Tue.4, H 0110 98 [16:55], 98
Ouyang, Yuyuan, Clemson University

Index of Authors

Tue.2, H 0104 78[c], 78[o]Tue.2, H 3006 75
Tue.3, H 0104 90[c], 90[o]Thu.2, H 0104 160 [11:35]
Overton, Michael, New York University
Tue.2, H 0105 79 [13:15]
Tue, 16:30, Memorial Session, H 0105 15[c], 15[o]Wed.1, H 1029 114
Ozdaglar, Asuman,
Tue.1, H 2053 67[o], 67
Thu.3, H 0107 169
Ozener, Okan, Ozyegin University
Thu.1, H 3004 143 [9:00], 143
Ozturk, Berk, Massachusetts Institute of Technology
Tue.2, H 2032 75 [13:15]
Padmanabhan, Divya, Singapore University of Technology
and Design
Tue.3, H 1058 93 [15:10]
Wed.2, H 3006 128
Palladino, Michele, Gran Sasso Science InstituteTue.1, H 0107 69[o]Tue.2, H 0112 82[o]Tue.3, H 0107 92[o]Tue.4, H 0107 103[o], 103 [17:20]
Wed.1, H 0111 116[c], 116[o]Wed.2, H 0111 127[c], 127[o]Wed.3, H 0107 139[c], 139[o]Pan, Bolin [canceled], UCL
Thu.3, H 1012 172 [13:55]
Pang, C.H. Jeffrey, National University of Singapore
Thu.2, H 1028 158 [10:45]
Pang, Jong-Shi, University of Southern California
Mon.1, H 0104 39[c], 39[o], 39 [11:00]
Tue, 9:00, Plenary Talk, H 0105 14[c]Tue.2, H 2013 73[c], 73[o], 73
Pankajakshan, Arun, University College London
Mon.1, H 3013 44 [11:00]
Pany, Gayatri, Singapore University of Technology and Design
Wed.1, H 3008 109 [11:30]
Papafitsoros, Kostas, Weierstrass Institute for Applied Analysis and Stochastics
Mon.1, H 0106 47[c], 47[o], 47 [11:50]
Mon.2, H 0106 58[o]Thu.1, H 3007 144
Papp, David, North Carolina State University
Tue.3, H 3006 87 [15:35]
Parpas, Panos, Imperial College London
Wed.2, H 2053 126[o], 126 [15:05]
Wed.3, H 3007 138[c], 138[o], 138, 138
Parrilo, Pablo [canceled], MIT
Tue.1, H 2053 67
Wed.3, H 2032 134 [16:00]
Pasadakis, Dimosthenis, Università della Svizzera italiana(USI)
Thu.3, H 3007 165[c], 165 [14:20]
Poster Session 24
Passacantando, Mauro, Università di Pisa
Thu.1, H 3025 147 [9:25]
Pasupathy, Raghu, Purdue University
Wed.2, H 2033 123 [14:15]
Patel, Vivak, University of Wisconsin-Madison
Tue.1, H 0104 72 [10:55]
Pattathil, Sarath, MIT
Thu.3, H 0107 169 [13:55]
Pearson, John, University of Edinburgh
Tue.3, H 2053 91 [15:35]
Tue.4, H 0111 102
Pecci, Filippo, Imperial College London

185

Wed.3, H 3007 138 [16:00]
Thu.2, H 3012 154
Peitz, Sebastian, Paderborn University
Mon.1, H 3013 44
Mon.2, H 3006 57 [13:45]
Pena, Javier, Carnegie Mellon University
Wed.2, H 0110 120 [15:05]
Peña-Ordieres, Alejandra, Northwestern University, Evanston
Mon.2, H 0105 55 [13:45]
Pereira Ferreira, Orizon, Federal University of Goias (UFG)
Thu.1, H 0107 150[c], 150[o], 150
Thu.2, H 2053 161[c], 161[o], 161 [11:10]
Pérez-Aros, Pedro, Universidad de O’Higgins
Thu.1, H 3012 153[c], 153 [9:25]
Pesquet, Jean-Christophe, University Paris Saclay
Wed.3, H 2038 141[c], 141 [16:50]
Peters, Benjamin, Otto von Guericke University Magdeburg
Tue.4, H 2032 98 [16:55]
Petrik, Marek, University of New Hampshire
Tue.2, H 3012 83 [14:05]
Pfeffer, Max, Max Planck Institute for Mathematics in the
Sciences
Wed.2, H 0105 124 [14:40]
Wed.3, H 1012 137
Pfeiffer, Laurent, University of Graz
Wed.2, H 0111 127 [14:40]
Piliouras, Georgios, SUTD
Thu.3, H 0107 169 [14:20]
Pillutla, Krishna, University of Washington
Tue.4, H 3004 97 [17:20]
Pinar, Mustafa C., Bilkent University
Thu.1, H 1012 152[c], 152 [9:50]
Pisciella, Paolo, NTNU-The Norwegian University of Scienceand Technology
Tue.1, H 1012 62 [10:55]
Plaumann, Daniel, TU Dortmund University
Tue.1, H 0110 63 [10:30], 63
Tue.2, H 0110 74
Ploskas, Nikolaos, University of Western Macedonia
Tue.4, H 2033 105 [16:30]
Pohl, Mathias, University of Vienna
Tue.4, H 3012 105 [16:55]
Poirion, Pierre-Louis, RIKEN InstituteTue.4, H 1012 101
Thu.2, H 0110 155 [11:35]
Pokutta, Sebastian, Georgia Tech
Tue.3, H 0105 85
Thu.3, H 3006 172 [13:30]
Pong, Ting Kei, The Hong Kong Polytechnic University
Tue.1, H 3025 68
Tue.2, H 3002 77[c], 77[o], 77
Wed.3, H 1012 137
Wed.3, H 3008 136[c], 136[o], 136 [16:50]
Porcelli, Margherita, University of Bologna
Mon.1, H 2038 44[o]Mon.2, H 2038 54[c], 54[o]Tue.1, H 2038 66[o]Tue.2, H 2038 77[o]Tue.2, H 2053 79
Tue.2, H 3025 80[c], 80 [14:05]
Tue.3, H 2038 88[o]Tue.4, H 2038 100[o]Wed.1, H 2033 112[o]Wed.2, H 2033 123[c], 123[o]Wed.3, H 2033 135[o]Thu.1, H 3025 147[o]Postek, Krzysztof, Erasmus University Rotterdam
Mon.1, H 1058 48 [11:00]

186

Index of Authors

Mon.2, H 3025 60[c], 60[o]Pougkakiotis, Spyridon, University of Edinburgh
Tue.4, H 0111 102[c], 102 [16:55]
Thu.2, H 2038 158
Povh, Janez, University of Ljubljana
Mon.1, H 3002 40[c], 40[o], 40
Mon.2, H 3002 51[o], 51, 51 [14:10]
Thu.3, H 2038 166
Prato, Marco, Università degli Studi di Modena e Reggio
EmiliaWed.3, H 2038 141 [16:25]
Thu.3, H 2032 169
Qi, Zhengling, The George Washington University
Tue.2, H 2013 73 [13:40]
Qian, Huajie, Columbia University
Wed.1, H 3006 117 [11:55]
Qian, Xun, KAUST
Tue.2, H 2033 84 [14:05]
Qu, Qing, New York University
Thu.3, H 3007 165 [13:30]
Qu, Zheng, The University of Hong Kong
Tue.2, H 2033 84 [13:15]
Quintana, Ernest, Martin Luther University Halle-Wittenberg
Tue.1, H 3013 66 [11:20]
Rabiei, Nima, American University of Iraq
Thu.2, H 1029 159 [10:45]
Radons, Manuel, TU Berlin
Thu.2, H 3012 154[c], 154 [11:10]
Rajawat, Ketan, Indian Institute of Technology Kanpur
Poster Session 23
Ralph, Daniel, University of Cambridge
Wed.3, H 2053 131 [16:00]
Rampazzo, Franco, University of Padova
Tue.4, H 0107 103
Wed.1, H 0111 116 [11:30]
Rauls, Anne-Therese, TU Darmstadt
Wed.3, H 0106 139 [16:25]
Rautenberg, Carlos, Humboldt-Universität zu Berlin / WIAS
Mon, 16:20, Best Paper Session, H 0105 13
Tue.3, H 0110 86[o], 86
Tue.4, H 0112 103[o], 103 [17:20]
Wed.1, H 0112 116[o]Wed.2, H 3013 121[o], 121
Ravier, Robert, Duke University
Thu.1, H 3007 144 [9:25]
Razaviyayn, Meisam, University of Southern California
Tue.2, H 2013 73 [14:05]
Rebegoldi, Simone, Università degli Studi di Modena e Reggio
EmiliaWed.3, H 2038 141
Thu.3, H 2032 169 [13:30]
Recht, Benjamin, University of California, Berkeley
Mon.2, H 0104 50[o], 50 [13:45]
Mon.2, H 0110 61
Tue.1, H 2013 62[c], 62[o]Redder, Adrian, Automatic Control Group / Paderborn University
Poster Session 24
Rehfeldt, Daniel, Technische Universität Berlin
Tue.3, H 0111 91[c], 91 [15:35]
Riccietti, Elisa, University of Toulouse
Wed.1, H 2053 115[c], 115[o], 115 [12:20]
Richtárik, Peter, King Abdullah University of science andtechnology (KAUST)
Tue.2, H 2033 84
Tue.3, H 2013 90
Wed.1, H 2013 114
Wed.2, H 0104 120 [14:15]

Richter, Robin, Georg-August Universität Göttingen
Thu.1, H 0112 151 [9:25]
Ridzal, Denis, Sandia National Laboratories
Wed.2, H 0112 128 [15:05]
Riener, Cordian, Uit
Wed.2, H 2032 122 [14:40]
Riis, Erlend Skaldehaug, University of Cambridge
Wed.3, H 0105 136 [16:25]
Rinaldi, Francesco, Università di Padova
Mon.1, H 2038 44[c], 44[o]Mon.2, H 2038 54[o]Tue.1, H 2038 66[o]Tue.2, H 2038 77[o], 77 [13:40]
Tue.2, H 2053 79
Tue.3, H 2038 88[o]Tue.4, H 2038 100[o]Wed.1, H 2033 112[o]Wed.2, H 2033 123[o]Wed.3, H 2033 135[o]Thu.1, H 3025 147[o], 147
Thu.2, H 3025 159[c]Thu.3, H 2038 166
Riquelme, Victor, Universidad Tecnica Federico Santa Maria
Mon.1, H 3012 46 [11:25]
Roberts, Lindon, University of Oxford
Wed.2, H 2033 123 [14:40]
Robinson, Daniel, Lehigh University
Tue.1, H 0105 67 [11:20]
Wed.2, H 1058 123[c], 123[o], 123, 123
Wed.3, H 3025 135[c]Rodomanov, Anton, UCLouvain
Tue.2, H 0105 79 [13:40]
Rodrigues, Sergio, RICAM
Thu.3, H 2013 171 [13:30]
Rodrigues Sampaio, Phillipe, Veolia Research and Innovation
Tue.4, H 2038 100 [17:20]
Romito, Francesco, Sapienza University of Rome
Wed.2, H 3008 124 [14:40]
Rontsis, Nikitas, University of Oxford
Tue.3, H 2053 91
Wed.3, H 1012 137 [16:00]
Roos, Ernst, Tilburg University
Mon.2, H 3025 60 [14:10]
Wed.3, H 3004 140
Roosta, Fred, University of Queensland
Mon.1, H 0105 45 [11:25]
Rosasco, Lorenzo, Universita’ di Genova and MIT
Tue.1, H 2013 62 [10:30]
Royer, Clément, University of Wisconsin-Madison
Tue.1, H 0105 67 [10:30]
Tue.2, H 2038 77 [13:15]
Wed.1, H 2013 114
Rujeerapaiboon, Napat, National University of Singapore
Tue.1, H 1058 70, 70
Wed.3, H 3006 141 [16:00]
Ruszczynski, Andrzej, Rutgers University
Mon, 16:20, Best Paper Session, H 0105 13[c]Tue.1, H 0104 72 [10:30]
Rutkowski, Krzysztof, Warsaw University of Technology
Wed.3, H 2038 141 [16:00]
Wed.3, H 3025 135
Ryu, Ernest, UCLA
Thu.1, H 1028 147[o]Thu.2, H 1028 158
Thu.3, H 1028 167[c], 167[o], 167 [14:20]
Sabach, Shoham, Technion-Israel Institute of Technology
Mon.1, H 1012 49[c], 49[o], 49 [11:50]
Tue.4, H 2033 105[o]Index of Authors

Thu.1, H 1012 152[o]Thu.3, H 1012 172[o]Sachs, Ekkehard, Universität Trier
Tue.4, H 0112 103 [16:30]
Sadeghi, Hamed [canceled], Lund University
Tue.3, H 2033 95 [15:10]
Safarina, Sena, Tokyo Institute of Technology
Mon.1, H 0112 42 [11:50]
Salim, Adil, King Abdullah University of Science and Technology (KAUST)
Tue.3, H 2013 90 [15:10]
Salomon, Ludovic, Polytechnique Montréal
Wed.1, H 2033 112 [11:55]
Salzo, Saverio, Istituto Italiano di Tecnologia
Wed.1, H 1028 111 [11:55]
Sanchez, Maria Daniela, Centro de Matemática de La Plata,
Universidad Nacional de La Plata
Thu.2, H 0106 160 [11:10]
Sanchez Licea, Gerardo, Facultad de Ciencias UNAM
Tue.3, H 0107 92 [15:35]
Sandilya, Ruchi, Weierstrass Institute for Applied Analysis andStochastics (WIAS)
Wed.2, H 3013 121 [15:05]
Sarabi, Ebrahim, Miami University, Ohio
Tue.1, H 1028 65
Tue.4, H 0110 98 [16:30]
Sartenaer, Annick, University of Namur
Tue, 16:30, Memorial Session, H 0105 15
Sassen, Josua, University of Bonn
Thu.2, H 2053 161 [10:45]
Saunders, Michael, Stanford University
Mon.1, H 2032 45[o], 45 [11:50]
Mon.2, H 2032 56[c], 56[o], 56
Saunderson, James, Monash University
Mon.1, H 2053 42[o]Sauter, Marta, University of Heidelberg
Mon.1, H 0107 39 [11:00]
Scarinci, Teresa, University of Vienna
Tue.1, H 0107 69 [10:30]
Schäfer Aguilar, Paloma, TU Darmstadt
Wed.2, H 0111 127 [14:15]
Scheinberg, Katya, Cornell University
Tue.1, H 0105 67 [10:55]
Tue, 16:30, Memorial Session, H 0105 15
Wed.2, H 2033 123
Schenk, Christina, Carnegie Mellon University
Mon.1, H 0107 39[o], 39 [11:50]
Mon.2, H 0107 50[c], 50[o]Schenk, Olaf, Università della Svizzera italiana
Thu.3, H 3007 165
Poster Session 24
Schiela, Anton, Universität Bayreuth
Thu.1, H 0112 151[o]Thu.2, H 0112 161[c], 161[o]Thu.3, H 0112 170[o], 170 [13:30]
Schindler, Kilian, EPFL
Tue.1, H 1058 70 [10:30]
Schmitt, Johann Michael, TU Darmstadt
Tue.3, H 0112 93 [15:35]
Schrempf, Konrad, University of Applied Sciences Upper Austria
Thu.2, H 2038 158[c], 158 [11:35]
Schultes, Johanna, University of Wuppertal
Poster Session 24
Schulz, Volker, Trier University
Tue.2, H 0106 81 [13:15]
Tue.4, H 0106 102
Schulze, Veronika, Paderborn University

187

Tue.3, H 0106 92 [15:10]
Schütz, Peter, Norwegian University of Science and Technology NTNU
Mon, 15:30, Semi-Plenary Talk, H 0104 13[c]Wed.3, H 2053 131 [16:25]
Thu.1, H 3004 143
Schwartz, Alexandra, TU Darmstadt
Mon.1, H 3007 41
Mon.1, H 3013 44[c], 44[o], 44 [11:50]
Mon.2, H 3013 55[o]Wed.1, H 3002 112[o]Thu.1, H 0111 148[o]Schweighofer, Markus, Universität Konstanz
Tue.1, H 0110 63[c], 63[o]Tue.2, H 0110 74[c], 74[o], 74 [13:40]
Schwing, Alex, University of Illinois at Urbana-Champaign
Mon.1, H 3004 40 [11:50]
Scieur, Damien, SAIT Montreal
Tue.3, H 0104 90 [15:35]
Scutari, Gesualdo, Purdue University
Mon.1, H 0104 39 [11:50]
Seidler, Henning, TU Berlin
Tue.1, H 2032 64 [10:30]
Shafieezadeh Abadeh, Soroosh, EPFL
Tue.1, H 1058 70
Tue.3, H 3012 94[c], 94[o], 94 [15:35]
Tue.4, H 1058 104
Shaked-Monderer, Naomi, The Max Stern Yezreel Valley
CollegeWed.3, H 0111 133 [16:00]
Shanbhag, Uday, Penn State University
Tue, 9:00, Plenary Talk, H 0105 14
Thu.1, H 0104 149
Thu.1, H 1012 152
Poster Session 24
Shao, Zhen, University of Oxford
Wed.2, H 2053 126 [14:40]
Sharma, Meenarli, Indian Institute of Technology Bombay
Tue.3, H 0112 93 [14:45]
Shi, Jianming, Tokyo University of Science
Wed.1, H 3012 118 [11:55]
Wed.2, H 3008 124
Shikhman, Vladimir, Chemnitz University of Technology
Wed.1, H 1058 111[c], 111 [12:20]
Poster Session 23
Shoemaker, Christine, National University of Singapore
Tue.3, H 2038 88 [14:45]
Shtern, Shimrit, Technion-Israel Institute of Tecnology
Mon.1, H 1058 48[c], 48[o], 48, 48 [11:50]
Tue.1, H 3012 71
Si, Nian, Stanford University
Tue.3, H 3012 94 [15:10]
Siddiqui, Sauleh, Johns Hopkins University
Tue.1, H 1012 62 [10:30]
Siebenborn, Martin, Universität Hamburg
Tue.1, H 0106 69[o]Tue.2, H 0106 81[c], 81[o]Tue.3, H 0106 92[c], 92[o]Tue.4, H 0106 102[o], 102 [16:55]
Silva, Paulo J. S., University of Campinas - UNICAMP
Thu.1, H 0106 149[c], 149 [9:50]
Thu.2, H 0106 160[c]Sim, Chee Khian, University of Portsmouth
Thu.2, H 2038 158 [10:45]
Singh, Abhishek, Indian Institute of Technology (BHU)
Varanasi
Wed.2, H 3013 121 [14:15]
Sinn, Rainer, Freie Universität Berlin

188

Index of Authors

Tue.1, H 0110 63
Tue.2, H 0110 74 [13:15]
Skipper, Daphne, United States Naval Academy
Tue.4, H 1012 101 [16:30], 101
Sliwak, Julie, RTE, Polytechnique Montréal, LIPN
Thu.1, H 2032 145 [9:25]
Slot, Lucas, CWI
Thu.3, H 2033 166 [13:55]
So, Anthony Man-Cho, The Chinese University of Hong Kong
Mon.2, H 0112 53[c], 53[o], 53, 53 [14:35]
Wed.1, H 3025 107
Soares Siqueira, Abel, Federal University of ParanáTue.2, H 0111 80[o]Soheili, Negar, University of Illinois at Chicago
Wed.2, H 0107 121[c], 121[o], 121 [15:05]
Soltani, Roya [canceled], Khatam University
Poster Session 24
Soltanolkotabi, Mahdi, University of Southern California
Mon.2, H 0104 50[c], 50[o]Tue.1, H 2013 62[o], 62 [10:55]
Sotirov, Renata, Tilburg University
Tue.4, H 2032 98
Wed.1, H 0107 110[c], 110[o], 110, 110
Souza, João Carlos, Federal University of Piauı́
Thu.1, H 0107 150 [9:25]
Speakman, Emily,
Tue.4, H 1012 101[o], 101
Spivak, Alexander, HIT - Holon Institute of Technology
Poster Session 24
Sra, Suvrit, MIT
Wed.1, H 0105 113 [11:30]
Wed.3, H 3002 132
Srivastava, Prateek R, University of Texas at Austin
Mon.1, H 3025 48 [11:50]
Staudigl, Mathias, Maastricht University
Mon.2, H 3007 52 [13:45]
Wed.2, H 2038 129[c], 129[o]Stengl, Steven-Marian, WIAS/HU Berlin
Tue.3, H 0110 86 [15:10]
Poster Session 24
Stewart, David, University of Iowa
Thu.1, H 3013 145 [9:25]
Stich, Sebastian U., EPFL
Tue.4, H 2013 97 [16:30]
Stingl, Michael, Friedrich-Alexander-Universität (FAU)
Erlangen-Nürnberg
Wed.2, H 3004 119 [14:15]
Stöger, Dominik, TU München, Fakultät für Mathematik
Thu.1, H 0112 151 [9:50]
Strasdat, Nico, TU Dresden
Wed.1, H 3013 109 [11:55]
Strugariu, Radu, Gheorghe Asachi Technical University of Iasi
Tue.2, H 3013 78
Tue.4, H 3013 101 [16:30]
Sturm, Kevin, TU Wien, Institut für Analysis and Scientific
ComputingTue.1, H 0106 69 [11:20]
Tue.2, H 0106 81
Sturt, Bradley, Massachusetts Institute of Technology
Mon.1, H 1058 48
Tue.1, H 3012 71 [10:30]
Subramanyam, Anirudh, Argonne National Laboratory
Mon.1, H 3025 48 [11:25]
Wed.2, H 3007 129[o], 129 [14:15]
Sun, Andy, Georgia Institute of Technology
Wed.1, H 0104 108[o], 108
Thu.1, H 0105 143[o]Sun, Cong, Beijing University of Posts and Telecommunications
Mon.2, H 1012 60[o], 60 [14:35]
Tue.2, H 3007 83[c], 83[o]Tue.4, H 3007 106[c], 106[o]Sun, Defeng, The Hong Kong Polytechnic University
Mon.1, H 0104 39 [11:25]
Tue.2, H 2013 73
Tue.2, H 3004 74, 74, 74
Tue.3, H 1029 88
Tue.4, H 3004 97
Wed.1, H 0110 108
Sun, Kaizhao, Georgia Institute of Technology
Wed.1, H 0104 108 [11:30]
Thu.1, H 0105 143[c]Sun, Ruoyu, UIUC
Mon.1, H 3004 40[o], 40 [11:00]
Sun, Yifan, UBC VancouverWed.1, H 2038 118 [11:55]
Surowiec, Thomas Michael, Philipps-Universität Marburg
Thu.1, H 2013 151[c], 151[o]Thu.2, H 2013 162[o], 162 [10:45]
Thu.3, H 2013 171[o]Sutter, Tobias, École Polytechnique Fédérale de Lausanne(EPFL)
Tue.1, H 0104 72
Tue.3, H 3012 94 [14:45]
Sutti, Marco, University of Geneva
Wed.2, H 0105 124 [14:15]
T. Khalil, Nathalie, Universidade do Porto Faculdade de Engenharia
Tue.2, H 0112 82 [13:15]
Takáč, Martin, Lehigh University
Tue.4, H 2013 97[o], 97 [16:55]
Wed.2, H 0104 120[c], 120[o]Wed.3, H 0104 131[o]Thu.2, H 3007 156
Takeda, Akiko, The University of Tokyo/RIKEN
Tue.3, H 3002 89 [14:45]
Wed.2, H 1012 125[o], 125
Wed.3, H 1012 137[o], 137
Thu.2, H 0110 155[c], 155[o], 155, 155Tam, Matthew K., University of Göttingen
Thu.2, H 1028 158[o]Thu.3, H 1028 167 [13:55]
Tammer, Christiane, Martin-Luther-University of HalleWittenberg
Tue.1, H 3013 66[o], 66, 66
Tue.2, H 3013 78[o], 78 [13:15]
Tue.3, H 3013 89[c], 89[o], 89
Tue.4, H 3013 101[o]Wed.1, H 3002 112
Tan, Sandra S. Y., NUS
Thu.3, H 2033 166[c], 166 [14:20]
Tanaka, Mirai, The Institute of Statistical Mathematics
Thu.1, H 2038 146 [9:00]
Tanaka, Tamaki, Niigata University
Tue.2, H 3013 78 [13:40]
Tang, Peipei, Zhejiang University City College
Wed.1, H 0110 108, 108 [11:55]
Tankaria, Hardik, Kyoto University
Wed.2, H 1029 125 [14:40]
Tappenden, Rachael, University of Canterbury
Thu.2, H 3007 156[c], 156 [11:35]
Taylor, Adrien, Inria/ENS Paris
Tue.4, H 3002 100[c], 100[o]Thu.1, H 1028 147[c], 147[o]Thu.2, H 1028 158 [11:35]

Index of Authors

Thu.3, H 1028 167[o]Teboulle, Marc, Tel-Aviv University
Mon, 9:30, Plenary Talk, H 0105 12[c]Mon.1, H 1012 49
Tue.1, H 3007 71
Tempone, Raul, RWTH Aachen
Wed.2, H 3012 130
Wed.3, H 3012 142
Thu.3, H 2053 170[c], 170 [14:20]
Poster Session 22
ten Eikelder, Stefan, Tilburg University, The Netherlands
Mon.2, H 3025 60 [14:35]
Tenbrinck, Daniel, Friedrich-Alexander-Universität ErlangenNürnberg (FAU)
Thu.1, H 0112 151
Thu.2, H 1012 163[c], 163 [11:35]
Thu.2, H 1029 159
Terlaky, Tamás, Lehigh University, Bethlehem, PA
Wed.1, H 3004 107[c], 107[o], 107 [12:20]
Wed.2, H 3004 119[c], 119[o]Themelis, Andreas, KU Leuven
Wed.3, H 3008 136 [16:25]
Wed.3, H 3025 135
Thu.3, H 1028 167
Thiele, Aurelie, Southern Methodist University
Thu.3, H 1058 171[c], 171[o], 171 [14:20]
Thünen, Anna, Institut für Geometrie und Praktische Mathematik (IGPM), RWTH Aachen University
Mon.1, H 3007 41[c], 41[o]Mon.2, H 3007 52[c], 52[o], 52 [14:35]
Timme, Sascha, Technische Universität Berlin
Mon.2, H 0111 52 [14:10]
Tin, Andrey, University of Southampton
Thu.2, H 3013 156 [11:10]
Tits, Andre, University of Maryland College Park
Tue.2, H 3025 80 [13:40]
Toader, Bogdan, Oxford University
Thu.3, H 1012 172[c], 172 [14:20]
Toh, Kim-Chuan, National University of Singapore
Tue.1, H 3006 64
Tue.2, H 2013 73 [13:15]
Tue.2, H 3004 74, 74, 74
Tue.3, H 1029 88
Tue.4, H 3004 97, 97
Wed, 9:00, Plenary Talk, H 0105 15[c]Wed.1, H 0110 108
Toint, Philippe L., University of Namur
Tue, 16:30, Memorial Session, H 0105 15
Wed.1, H 1012 113 [11:55]
Wed.1, H 2053 115
Tovey, Robert, University of Cambridge
Tue.3, H 3007 94 [15:10]
Tran-Dinh, Quoc, University of North Carolina at Chapel Hill
Mon.1, H 1029 43[c], 43[o]Mon.2, H 1029 54 [14:35]
Tue.1, H 1029 65[c], 65[o]Tue.2, H 1029 76[o]Tue.4, H 3004 97
Tret’yakov, Alexey, University of Podlasie in Siedlce
Thu.1, H 0106 149 [9:00]
Thu.2, H 0106 160
Trichakis, Nikos, MIT
Thu.1, H 1058 152[c], 152[o], 152 [9:50]
Thu.3, H 3006 172
Tröltzsch, Fredi, Technische Universität Berlin
Mon.1, H 2013 47[o], 47 [11:00]
Mon.2, H 2013 58[c], 58[o]Tue.1, H 0112 70[o]189

Truden, Christian, Alpen-Adria-Universität Klagenfurt
Wed.1, H 0107 110 [11:55]
Tsuchiya, Takashi, National Graduate Institute for Policy Studies
Wed.3, H 0111 133 [16:25]
Tu, Stephen, UC Berkeley
Mon.2, H 0110 61 [13:45]
Tuncel, Levent, University of Waterloo
Tue.3, H 3006 87
Tue.4, H 3006 99 [16:30]
Ulbrich, Michael, Technical University of Munich
Mon.1, H 1028 43
Tue.1, H 0106 69
Wed.1, H 0106 115
Thu.1, H 2013 151[o]Thu.2, H 2013 162[o]Thu.3, H 2013 171[c], 171[o], 171Thu, 16:15, Plenary Talk, H 0105 19
Ulbrich, Stefan, TU Darmstadt
Tue.2, H 1012 73
Tue.3, H 0112 93, 93
Wed.1, H 0112 116
Wed.1, H 3013 109
Wed.2, H 0111 127
Wed.2, H 0112 128 [14:40]
Wed.3, H 0106 139
Thu.1, H 2013 151[o]Thu.2, H 2013 162[o]Thu.2, H 3012 154
Thu.2, H 3025 159
Thu.3, H 2013 171[o]Thu, 15:15, Semi-Plenary Talk, H 0104 18[c]Uribe, Cesar A, MIT
Wed.3, H 3002 132[c], 132[o], 132, 132
Uschmajew, André, Max Planck Institute for Mathematics in
the Sciences
Wed.1, H 0105 113[c], 113[o]Wed.2, H 0105 124[o]Wed.3, H 1012 137 [16:25]
Vaes, Julien, University of Oxford
Tue.4, H 1028 96 [16:30]
Vaisbourd, Yakov, Tel Aviv University
Mon.1, H 1012 49 [11:25]
Valkonen, Tuomo, Escuela Politécnica Nacional
Mon.2, H 0106 58
Tue.1, H 1029 65 [11:20]
Van Barel, Andreas, KU Leuven
Thu.2, H 2013 162 [11:35]
van Bloemen Waanders, Bart, Sandia National Laboratory
Tue.3, H 0112 93
Thu.1, H 2053 150 [9:25]
van den Berg, Ewout, IBM T.J. Watson
Tue.4, H 1029 99 [16:30]
Van Mai, Vien, KTH Royal Institue of Technology
Tue.1, H 1029 65 [10:55]
Van Parys, Bart P.G., Massachusetts Institute of Technology
Thu.2, H 3006 162 [11:10]
Vandenberghe, Lieven, UCLA
Tue.4, H 3006 99
Wed, 17:30, Semi-Plenary Talk, H 0104 17
Vandereycken, Bart, University of Geneva
Wed.1, H 0105 113[o]Wed.2, H 0105 124[c], 124[o]Vanli, Nuri, Massachusetts Institute of Technology
Tue.1, H 2053 67[c], 67[o], 67 [11:20]
Vannieuwenhoven, Nick, KU Leuven
Wed.1, H 0105 113 [11:55]

190

Index of Authors

Varvitsiotis, Antonios, National University of Singapore(NUS)
Thu.3, H 2033 166 [13:30], 166
Vayanos, Phebe, University of Southern California
Mon.2, H 1058 59
Thu.1, H 1058 152 [9:25]
Veldt, Nate, Cornell University
Tue.1, H 3004 63 [10:30]
Verma, Deepanshu, George Mason University
Wed.1, H 0112 116 [11:30]
Vidal-Nunez, Jose, TU Chemnitz
Thu.1, H 0112 151
Thu.2, H 1029 159[c], 159 [11:35]
Vidanagamachchi, Sugandima Mihirani, University of
RuhunaThu.3, H 3004 164 [13:30]
Vieira, Douglas, EnacomThu.2, H 3025 159 [11:35]
Vielma, Juan Pablo, Massachusetts Institute of Technology
Mon.1, H 0111 41[c], 41[o], 41
Mon.2, H 0111 52[c], 52[o], 52
Vilches, Emilio, Universidad de O’Higgins
Wed.3, H 0107 139 [16:00]
Viorel, Adrian, Tehnical University of Cluj-Napoca
Poster Session 24
Vogt, Thomas, University of Lübeck
Thu.2, H 0112 161 [11:10]
Vollmann, Christian, Trier University
Tue.4, H 0106 102 [17:20]
Vorobyov, Sergiy A., Aalto University
Wed.1, H 3025 107 [11:30]
Vorontsova, Evgeniya, Université Grenoble Alpes
Thu.1, H 3025 147 [9:50]
Vu, Bang Cong, École Polytechnique Fédérale de Lausanne(EPFL)
Wed.1, H 1028 111 [11:30]
Vuong, Phan Tu, University of Vienna
Tue.2, H 1029 76 [14:05]
Thu.2, H 3013 156
Wachsmuth, Gerd, BTU Cottbus-Senftenberg
Wed.3, H 0106 139 [16:00]
Wadbro, Eddie, Umeå University
Tue.1, H 0106 69 [10:30]
Tue.4, H 0106 102
Wai, Hoi-To, CUHK
Mon.2, H 0110 61 [14:10]
Walter, Anna, TU Darmstadt
Wed.1, H 3013 109 [12:20]
Walter, Daniel, RICAM
Wed.1, H 0106 115 [11:30]
Wang, Alex, Carnegie Mellon University
Thu.2, H 2032 157 [11:10]
Wang, Chengjing, Southwest Jiaotong University
Wed.1, H 0110 108 [11:30], 108
Wang, Di, Georgia Institute of Technology
Tue.1, H 3004 63[o]Wang, Fei, Royal Institute of Technology (KTH)
Thu.1, H 2033 146[c], 146 [9:25]
Wang, Hao, ShanghaiTech University
Wed.3, H 1029 137 [16:50]
Wang, Jie, Peking University
Tue.1, H 2032 64 [11:20]
Wang, Liping, Nanjing University of Aeronautics and Astronautics
Mon.2, H 1012 60 [14:10]
Wang, Meihua, Xidian University
Poster Session 24

Wang, Xiaoyu, Academy of Mathematics and Systems Science,
Chinese Academy of Sciences
Tue.2, H 3007 83 [13:40]
Wang, Yanfei, Chinese Academy of Sciences
Tue.2, H 3007 83 [14:05]
Wang, Yuzhu, University of Tsukuba
Mon.1, H 0112 42 [11:00]
Warma, Mahamadii, University of Puerto Rico, Rio Piedras
CampusTue.4, H 0112 103[o], 103 [16:55]
Wed.1, H 0112 116[c], 116[o], 116
Wei, Ke [canceled], Fudan University
Thu.3, H 0105 164 [13:55]
Weinmann, Andreas, Hochschule Darmstadt
Thu.2, H 0112 161 [10:45]
Weisser, Tillmann, Los Alamos National Laboratory
Mon.2, H 0111 52 [14:35]
Weldeyesus, Alemseged Gebrehiwot, The University of Edinburgh
Wed.1, H 3004 107
Wed.2, H 3004 119 [14:40]
Welker, Kathrin, Helmut-Schmidt-Universität Hamburg
Tue.1, H 0106 69[c], 69[o]Tue.2, H 0106 81[o], 81 [13:40]
Tue.3, H 0106 92[o]Tue.4, H 0106 102[c], 102[o]Wen, Zaiwen, Peking University
Mon.2, H 1012 60
Tue.4, H 1029 99[o], 99
Wiegele, Angelika, Alpen-Adria-Universität Klagenfurt
Tue.3, H 2032 86[c], 86[o], 86
Thu.1, H 2032 145 [9:00]
Wiesemann, Wolfram, Imperial College Business School
Mon.2, H 1058 59
Tue.1, H 1058 70
Tue.2, H 3012 83, 83
Wed, 10:15, Semi-Plenary Talk, H 0105 16[c]Wed.3, H 3004 140
Wed.3, H 3006 141 [16:25]
Thu.2, H 3006 162
Wild, Stefan, Argonne National Laboratory
Mon.1, H 2038 44[o], 44
Mon.2, H 0105 55 [14:10]
Mon.2, H 2038 54[o]Tue.1, H 2038 66[c], 66[o], 66
Tue.2, H 2038 77[o]Tue.3, H 2038 88[o]Tue.4, H 2038 100[o]Wed.1, H 2033 112[o]Wed.2, H 2033 123[o]Wed.3, H 2033 135[c], 135[o]Thu.1, H 3025 147[o]Winckler, Malte, University of Duisburg-Essen
Mon.2, H 2013 58 [14:35]
Wirsching, Leonard, Heidelberg University
Mon.1, H 0107 39 [11:25]
Wolenski, Peter, Louisiana State University
Wed.1, H 0111 116 [11:55]
Wolkowicz, Henry, University of Waterloo
Tue.1, H 3025 68
Tue.2, H 2053 79 [13:15]
Tue, 16:30, Memorial Session, H 0105 15
Wed.1, H 0107 110
Wong, Elizabeth, UC San Diego
Mon.1, H 2032 45 [11:00]
Wylie, Calvin, Cornell University
Wed.2, H 1028 122 [14:15]
Xiao, Lin, Microsoft ResearchIndex of Authors

Tue.3, H 0105 85 [15:35]
Xie, Jingui, University of Science and Technology of China
Wed.2, H 3006 128 [14:40]
Xie, Yue, University of Wisconsin-MadisonThu.1, H 1012 152 [9:25]
Poster Session 24
Xu, Fengmin, Xi’an Jiaotong University
Tue.1, H 3025 68
Wed.1, H 3012 118[c], 118[o], 118 [12:20]
Wed.2, H 3012 130[c], 130[o]Wed.3, H 3012 142[c], 142[o]Poster Session 24
Xu, Guanglin [canceled],
Wed.1, H 3007 117 [11:55]
Wed.2, H 3006 128
Xu, Yangyang, Rensselaer Poly. InstituteMon.2, H 1029 54[o]Tue.2, H 3006 75 [14:05]
Thu.1, H 0104 149[o]Thu.2, H 0104 160[c], 160[o]Xu, Yifeng, Shanghai Normal University
Mon.1, H 2013 47 [11:25]
Yamada, Isao, Tokyo Institute of Technology
Wed.2, H 1028 122 [14:40]
Yamada, Syuuji, Niigata University
Wed.2, H 3008 124[c], 124 [15:05]
Yamashita, Makoto, Tokyo Institute of Technology
Mon.1, H 0112 42[c], 42[o], 42
Tue.1, H 3006 64[o], 64 [11:20]
Yan, Zhenzhen, Nanyang Technological University
Tue.4, H 3012 105 [16:30]
Wed.1, H 3007 117 [11:30]
Yang, Lei, National University of Singapore
Tue.2, H 3004 74 [14:05]
Yang, Minghan, Peking University
Tue.4, H 1029 99 [16:55]
Yang, Tianbao, University of Iowa
Tue.2, H 1029 76
Wed.2, H 0107 121
Wed.2, H 0110 120[o]Wed.3, H 0110 132[c], 132[o]Thu.1, H 0104 149 [9:25]
Yang, Yang, Fraunhofer ITWM
Wed.1, H 3025 107 [11:55]
Yang, Zhuoran, Princeton University
Thu.2, H 0105 155 [11:10]
Yanikoglu, Ihsan, Ozyegin University
Tue.2, H 3012 83 [13:15]
Yashtini, Maryam, Georgetown University
Thu.2, H 1029 159 [11:10]
Yin, Peng-Yeng, National Chi Nan University
Poster Session 25
Yin, Wotao, University of California, Los Angeles
Wed.1, H 0104 108[c], 108[o]Thu.1, H 0105 143[o], 143 [9:50]
Thu.3, H 1028 167
Ying, Yiming, SUNY Albany
Wed.2, H 0110 120 [14:15]
Yoshise, Akiko, University of Tsukuba
Mon.1, H 0112 42[o], 42, 42
Tue.1, H 3006 64[c], 64[o]Yousept, Irwin, University of Duisburg-Essen
Mon.1, H 2013 47[c], 47[o], 47, 47 [11:50]
Mon.2, H 2013 58[o]Tue.1, H 0112 70[c], 70[o]Yu, Han, University of Southern California
Mon.2, H 1058 59 [13:45]
Yu, Hao, Alibaba Group (U.S.) Inc

191

Thu.2, H 0104 160 [10:45]
Yu, Huizhen, University of Alberta
Mon.1, H 0110 49 [11:25]
Yu, Peiran, The Hong Kong Polytechnic University
Tue.2, H 3002 77 [13:15]
Yuan, Yaxiang, State Key Laboratory of Scientific/Engineering
Computing, Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics andSystems Science, Chinese Academy of Sciences
Mon.2, H 1012 60 [13:45], 60
Tue.2, H 3007 83
Tue.4, H 3007 106
Yue, Man-Chung, Imperial College London
Tue.1, H 1058 70[c], 70[o]Tue.2, H 3012 83[c], 83[o]Thu.2, H 3006 162 [10:45]
Yuruk, Oguzhan, TU Braunschweig
Wed.1, H 2032 110 [11:30]
Zeile, Clemens, Otto-von-Guericke University Magdeburg
Thu.1, H 2053 150 [9:00]
Zemkoho, Alain, University of Southampton
Thu.2, H 3013 156[c], 156[o], 156 [11:35]
Thu.3, H 3013 165[c], 165[o]Žerovnik, Janez, University of Ljubljana
Mon.1, H 3002 40 [11:50]
Zhang, Jingzhao,
Wed.3, H 3002 132 [16:50]
Zhang, Shuzhong, University of Minnesota, joint appointment
with Institute of Data and Decision Analytics, The Chinese
University of Hong Kong, Shenzhen
Mon.2, H 3004 51[c], 51[o], 51 [14:10], 51
Wed.1, H 3025 107
Zhang, Xiaoqun, Shanghai Jiao Tong University
Thu.1, H 1028 147 [9:50]
Zhang, Yangjing, National University of Singapore
Tue.2, H 3004 74 [13:15]
Zhang, Yin, The Chinese University of Hong Kong, Shenzhen
Mon.2, H 3004 51 [13:45]
Zhang, Yuqian, Cornell University
Wed.2, H 0105 124 [15:05]
Zhang, Zaikun, Hong Kong Polytechnic University
Tue.1, H 2038 66 [10:55]
Zhao, Lei, Shanghai Jiao Tong University
Thu.2, H 3007 156 [10:45]
Zhao, Renbo [canceled], Massachusetts Institute of Technology
Tue.2, H 3006 75 [13:15]
Wed.3, H 3006 141
Zhao, Xinyuan, Beijing University of Technology
Tue.3, H 1029 88 [14:45]
Zhen, Jianzhe (Trevor), EPFL
Wed.3, H 3004 140 [16:25], 140
Zheng, Zhichao, Singapore Management University
Wed.1, H 3007 117[c], 117[o]Wed.2, H 3006 128[c], 128[o]Zhou, Wenwen, SAS Institute Inc.
Wed.3, H 1029 137 [16:25]
Zhou, Yi,
Tue.2, H 0104 78[o]Tue.3, H 0104 90[o]Zhou, Zirui, Hong Kong Baptist University
Tue.3, H 3002 89[c], 89[o]Thu.2, H 0110 155 [10:45]
Zhu, Daoli, Shanghai Jiao Tong University
Thu.2, H 3007 156
Thu.3, H 0107 169 [13:30]
Zhu, Jia-Jie, Max Planck Institute for Intelligent Systems
Poster Session 25

192

Index of Authors

Zhu, Zhihui, Johns Hopkins University
Wed.2, H 1058 123 [14:40]
Zou, Benteng, University of Luxembourg
Thu.2, H 3004 154 [10:45]
Zuluaga, Luis, Lehigh University
Tue.3, H 1012 85
Tue.4, H 2032 98[c], 98[o]Wed.1, H 3004 107
Wed.3, H 2053 131[c], 131 [16:50]