From bazara at sandia.gov  Thu Aug  8 11:59:50 2002
From: bazara at sandia.gov (Zaragoza, Barbara)
Date: Mon Oct 23 09:14:12 2006
Subject: [CSRI/CSMR Seminars] CSRI Seminar Series - Todd S. Munson - August 19, 2002
Message-ID: <A6AA130076E73D44AB07AEA7829073DB0736D290@es09snlnt>

Computational Sciences and Mathematics Research (CSMR) 
Presents

A Study in Support Vector Machines

Todd S. Munson
Argonne National Laboratory

Date: 	Monday, August 19, 2002, 10 a.m. (PST) 
Location:	Bldg. 921, Rm. 137(Sandia-CA)/Building 980, Rm. 95
(Sandia-NM)

ABSTRACT: We discuss interior-point and semismooth methods for solving
quadratic programming problems with a small number of linear constraints
where the quadratic term consists of a low-rank update to a positive
semi-definite matrix.  Several formulations of the support vector machine, a
technique employed by the machine learning community for supervised
learning, fit into this category.  A related example is the Huber regression
problem, which can also be posed as a quadratic program with the desired
properties.

Support vector machines can be used, for example, when determining whether a
tumor is malignant or benign.  An interesting feature of the problems we
consider is the volume of data, which can lead to quadratic programs with
between 5 and 100 million variables and, if written explicitly, a dense $Q$
matrix.  The implementations of the two algorithms use linear algebra
specialized for the support vector machine application.  For the targeted
massive problems, all data is stored out-of-core and we overlap computation
and I/O to reduce overhead.  Results are reported for several linear support
vector machine formulations demonstrating that the algorithms developed are
reliable and scalable.

ABOUT THE SPEAKER:	Todd Munson works at Argonne National Laboratories
in the Mathematics and Computer Science Division.  His primary research
focus is on algorithms and applications of complementarity.  He has worked
on two generalized Newton methods for solving these problems, one based on
the Fischer-Burmeister reformulation and the other on the normal map
reformulation.  Each of these reformulations results in a nonsmooth system
of equations to be solved. Implementations of these methods are available in
the SEMI and PATH codes.  Todd's recent work has been on a project to
develop methods for solving optimization problems governed by partial
differential equations.  These are large nonlinear programming problems
where parallel algorithms are needed.  This work is part of the TOPS center.
Todd received his Ph.D. in Computer Science from the University of Wisconsin
in 2000 and has been a postdoctoral researcher at Argonne since then.

Contact:  	Tamara Kolda (8950), (925) 294-4769, tgkolda@sandia.gov 


This seminar series is hosted by the CSMR Department at Sandia National Labs
in Livermore, CA. For more information on upcoming events in The CSMR
Department, visit http://csmr.ca.sandia.gov/news.html.

For more information on The Computer Science Research Institute (CSRI) at
Sandia National Labs in Albuquerque, NM, visit
http://www.cs.sandia.gov/CSRI.



From bazara at sandia.gov  Mon Aug 12 15:12:45 2002
From: bazara at sandia.gov (Zaragoza, Barbara)
Date: Mon Oct 23 09:14:12 2006
Subject: [CSRI/CSMR Seminars] CSRI Seminar Series - Professor Anne Greenbaum - August 22, 2002
Message-ID: <A6AA130076E73D44AB07AEA7829073DB0736D2A6@es09snlnt>

The Computer Science Research Institute (CSRI)

Presents

Theory and Applications of the Polynomial Numerical Hull

Professor Anne Greenbaum
University of Washington
Math Department

Date: 	Thursday, August 22, 2002 - 1:00 p.m.  (PST) 
Location:	Bldg. 921, Rm. 137(Sandia-CA) / Bldg. 980, Rm. 24
(Sandia-NM)
 	

ABSTRACT: The asymptotic behavior of a matrix or linear operator is governed
by its eigenvalues, but, in general, the transient behavior is not.  Yet
this transient behavior is often important in physical applications such as
convection and fluid flow and in numerical algorithms such as finite
difference schemes and iterative methods for solving linear systems.  In
this talk we consider sets associated with a matrix $A$ that are designed to
give more information than the spectrum alone can provide about this
transient behavior.  Specifically, we discuss the {\em polynomial numerical
hull of degree $k$}, which is defined as the set of points $z$ in the
complex plane for which $\| p(A) \| \geq | p(z) |$ for all polynomials $p$
of degree $k$ or less.  Geometrical properties and methods for computing
these sets are discussed.  As a sample application, we show how these sets
can be used to predict cutoff phenomena in Markov chains, such as the
much-publicized result that it takes seven riffle shuffles to randomize a
deck of cards.

ABOUT THE SPEAKER:  Professor Anne Greenbaum is a member of the University
of Washington, Mathematics Department since 1997.  Prior to this position,
she was a research professor at the Courant Institute of Mathematical
Sciences and a Mathematician at LLNL.   Professor Greenbaum's professional
activities include being a member of the SIAM Council and SIAM Program
Committee.  She is the author of three books, the most recent one,
"Iterative Methods for Solving Linear Systems"; she has also refereed
numerous publications.  Professor Greenbaum received both her Ph.D and
Master's degrees from the University of California, Berkeley

Contact:  	Victoria Howle, (8950), (925) 294-2204, vehowle@sandia.gov 

This seminar series is hosted by the CSMR Department at Sandia National Labs
in Livermore, CA. For more information on upcoming events in The CSMR
Department, visit http://csmr.ca.sandia.gov/news.html.  

For more information on The Computer Science Research Institute (CSRI) at
Sandia National Labs in Albuquerque, NM, visit
http://www.cs.sandia.gov/CSRI.



From bazara at sandia.gov  Fri Aug 16 11:54:46 2002
From: bazara at sandia.gov (Zaragoza, Barbara)
Date: Mon Oct 23 09:14:12 2006
Subject: [CSRI/CSMR Seminars] Reminder - CSRI Seminar Speaker - Todd Munson
Message-ID: <A6AA130076E73D44AB07AEA7829073DB0736D2CC@es09snlnt>

Computational Sciences and Mathematics Research (CSMR) 
Presents

A Study in Support Vector Machines

Todd S. Munson
Argonne National Laboratory

Date: 	Monday, August 19, 2002, 10 a.m. (PST) 
Location:	Bldg. 921, Rm. 137(Sandia-CA)/Building 980, Rm. 95
(Sandia-NM)

ABSTRACT: We discuss interior-point and semismooth methods for solving
quadratic programming problems with a small number of linear constraints
where the quadratic term consists of a low-rank update to a positive
semi-definite matrix.  Several formulations of the support vector machine, a
technique employed by the machine learning community for supervised
learning, fit into this category.  A related example is the Huber regression
problem, which can also be posed as a quadratic program with the desired
properties.

Support vector machines can be used, for example, when determining whether a
tumor is malignant or benign.  An interesting feature of the problems we
consider is the volume of data, which can lead to quadratic programs with
between 5 and 100 million variables and, if written explicitly, a dense $Q$
matrix.  The implementations of the two algorithms use linear algebra
specialized for the support vector machine application.  For the targeted
massive problems, all data is stored out-of-core and we overlap computation
and I/O to reduce overhead.  Results are reported for several linear support
vector machine formulations demonstrating that the algorithms developed are
reliable and scalable.

ABOUT THE SPEAKER:	Todd Munson works at Argonne National Laboratories
in the Mathematics and Computer Science Division.  His primary research
focus is on algorithms and applications of complementarity.  He has worked
on two generalized Newton methods for solving these problems, one based on
the Fischer-Burmeister reformulation and the other on the normal map
reformulation.  Each of these reformulations results in a nonsmooth system
of equations to be solved. Implementations of these methods are available in
the SEMI and PATH codes.  Todd's recent work has been on a project to
develop methods for solving optimization problems governed by partial
differential equations.  These are large nonlinear programming problems
where parallel algorithms are needed.  This work is part of the TOPS center.
Todd received his Ph.D. in Computer Science from the University of Wisconsin
in 2000 and has been a postdoctoral researcher at Argonne since then.

Contact:  	Tamara Kolda (8950), (925) 294-4769, tgkolda@sandia.gov 


This seminar series is hosted by the CSMR Department at Sandia National Labs
in Livermore, CA. For more information on upcoming events in The CSMR
Department, visit http://csmr.ca.sandia.gov/news.html.

For more information on The Computer Science Research Institute (CSRI) at
Sandia National Labs in Albuquerque, NM, visit
http://www.cs.sandia.gov/CSRI.



From bazara at sandia.gov  Tue Aug 20 15:35:12 2002
From: bazara at sandia.gov (Zaragoza, Barbara)
Date: Mon Oct 23 09:14:12 2006
Subject: [CSRI/CSMR Seminars] Reminder:  CSRI Speaker Series - Anne Greenbaum
Message-ID: <A6AA130076E73D44AB07AEA7829073DB0736D2F1@es09snlnt>

The Computer Science Research Institute (CSRI)

Presents

Theory and Applications of the Polynomial Numerical Hull

Professor Anne Greenbaum
University of Washington
Math Department

Date: 	Thursday, August 22, 2002 - 1:00 p.m.  (PST) 
Location:	Bldg. 921, Rm. 137(Sandia-CA) / Bldg. 980, Rm. 24
(Sandia-NM)
 	

ABSTRACT: The asymptotic behavior of a matrix or linear operator is governed
by its eigenvalues, but, in general, the transient behavior is not.  Yet
this transient behavior is often important in physical applications such as
convection and fluid flow and in numerical algorithms such as finite
difference schemes and iterative methods for solving linear systems.  In
this talk we consider sets associated with a matrix $A$ that are designed to
give more information than the spectrum alone can provide about this
transient behavior.  Specifically, we discuss the {\em polynomial numerical
hull of degree $k$}, which is defined as the set of points $z$ in the
complex plane for which $\| p(A) \| \geq | p(z) |$ for all polynomials $p$
of degree $k$ or less.  Geometrical properties and methods for computing
these sets are discussed.  As a sample application, we show how these sets
can be used to predict cutoff phenomena in Markov chains, such as the
much-publicized result that it takes seven riffle shuffles to randomize a
deck of cards.

ABOUT THE SPEAKER:  Professor Anne Greenbaum is a member of the University
of Washington, Mathematics Department since 1997.  Prior to this position,
she was a research professor at the Courant Institute of Mathematical
Sciences and a Mathematician at LLNL.   Professor Greenbaum's professional
activities include being a member of the SIAM Council and SIAM Program
Committee.  She is the author of three books, the most recent one,
"Iterative Methods for Solving Linear Systems"; she has also refereed
numerous publications.  Professor Greenbaum received both her Ph.D and
Master's degrees from the University of California, Berkeley

Contact:  	Victoria Howle, (8950), (925) 294-2204, vehowle@sandia.gov 

This seminar series is hosted by the CSMR Department at Sandia National Labs
in Livermore, CA. For more information on upcoming events in The CSMR
Department, visit http://csmr.ca.sandia.gov/news.html.  

For more information on The Computer Science Research Institute (CSRI) at
Sandia National Labs in Albuquerque, NM, visit
http://www.cs.sandia.gov/CSRI.