convergent difference schemes for degenerate elliptic and parabolic equations hamilton jacobi equations and free boundary problems convergent numerical schemes for degenerate elliptic partial differential equations are constructed and implemented simple conditions are identified which ensure that nonlinear finite difference schemes are monotone and nonexpansive in the maximum norm explicit schemes endowed with an explicit cfl condition are built for time dependent equations and are used to solve stationary equations iteratively explicit and implicit formulations of monotonicity for first and second order equations are unified bounds on orders of accuracy are established an example of a scheme which is stable but nonmonotone and nonconvergent is presented schemes for hamilton jacobi equations obstacle problems one phase free boundary problems and stochastic games are built and computational results are presented

on extension results for n cyclically monotone operators in reflexive banach spaces abstract in this paper we provide some extension results for n cyclically monotone operators in reflexive banach spaces by making use of the fenchel duality in this way we give a positive answer to a question posed by bauschke and wang 2007 4

grothendieck s theorem past and present probably the most famous of grothendieck s contributions to banach space theory is the result that he himself described as the fundamental theorem in the metric theory of tensor products that is now commonly referred to as grothendieck s theorem gt in short or sometimes as grothendieck s inequality this had a major impact first in banach space theory roughly after 1968 then later on in c algebra theory roughly after 1978 more recently in this millennium a new version of gt has been successfully developed in the framework of operator spaces or non commutative banach spaces in addition gt independently surfaced in several quite unrelated fields in connection with bell s inequality in quantum mechanics in graph theory where the grothendieck constant of a graph has been introduced and in computer science where the grothendieck inequality is invoked to replace certain np hard problems by others that can be treated by semidefinite programming and hence solved in polynomial time in this expository paper we present a review of all these topics starting from the original gt we concentrate on the more recent developments and merely outline those of the first banach space period since detailed accounts of that are already available for instance the author s 1986 cbms notes

fitzpatrick functions and continuous linear monotone operators the notion of a maximal monotone operator is crucial in optimization as it captures both the subdifferential operator of a convex lower semicontinuous and proper function and any not necessarily symmetric continuous linear positive operator it was recently discovered that most fundamental results on maximal monotone operators allow simpler proofs utilizing fitzpatrick functions in this paper we study fitzpatrick functions of continuous linear monotone operators defined on a hilbert space a novel characterization of skew operators is presented a result by brezis and haraux is reproved using the fitzpatrick function we investigate the fitzpatrick function of the sum of two operators and we show that a known upper bound is actually exact in finite dimensional and more general settings cyclic monotonicity properties are also analyzed and closed forms of the fitzpatrick functions of all orders are provided for all rotators in the euclidean plane

hardness and approximation results for l p ball constrained homogeneous polynomial optimization problems in this paper we establish hardness and approximation results for various lp ball constrained homogeneous polynomial optimization problems where p 2 specifically we prove that for any given d 3 and p 2 both the problem of optimizing a degree d homogeneous polynomial over the lp ball and the problem of optimizing a degree d multilinear form regardless of its super symmetry over lp balls are np hard on the other hand we show that these problems can be approximated to within a factor of log n d 2 p nd 2 1 in deterministic polynomial time where n is the number of variables we further show that with the help of randomization the approximation guarantee can be improved to log n n d 2 1 which is independent of p and is currently the best for the aforementioned problems our results unify and generalize those in the literature which focus either on the quadratic case or the case where p 2 we believe that the wide array of tools used in this paper will have further

a comparison of constraint qualifications in infinite dimensional convex programming in this paper the relationships between various constraint qualifications for infinite dimensional convex programs are investigated using robinson s refinement of the duality result of rockafellar it is demonstrated that the constraint qualification proposed by rockafellar provides a systematic mechanism for comparing many constraint qualifications as well as establishing new results in different topological environments

an efficient numerical method for the solution of the l_2 optimal mass transfer problem in this paper we present a new computationally efficient numerical scheme for the minimizing flow approach for the computation of the optimal l 2 mass transport mapping in contrast to the integration of a time dependent partial differential equation proposed in s angenent s haker and a tannenbaum siam j math anal 35 2003 pp 61 97 we employ in the present work a direct variational method the efficacy of the approach is demonstrated on both real and synthetic data

block tensors and symmetric embeddings abstract well known connections exist between the singular value decomposition of a matrix a and the schur decomposition of its symmetric embedding sym a 0 a a t 0 in particular if is a singular value of a then and are eigenvalues of the symmetric embedding the top and bottom halves of sym a s eigenvectors are singular vectors for a power methods applied to a can be related to power methods applied to sym a the rank of sym a is twice the rank of a in this paper we develop similar connections for tensors by building on l h lim s variational approach to tensor singular values and vectors we show how to embed a general order d tensor a into an order d symmetric tensor sym a through the embedding we relate power methods for a s singular values to power methods for sym a s eigenvalues finally we connect the multilinear and outer product rank of a to the multilinear and outer product rank of sym a

grothendieck type inequalities in combinatorial optimization we survey connections of the grothendieck inequality and its variants to combinatorial optimization and computational complexity 2011 wiley periodicals inc

fitzpatrick functions cyclic monotonicity and rockafellar s antiderivative several deeper results on maximal monotone operators have recently found simpler proofs using fitzpatrick functions in this paper we study a sequence of fitzpatrick functions associated with a monotone operator the first term of this sequence coincides with the original fitzpatrick function and the other terms turn out to be useful for the identification and characterization of cyclic monotonicity properties it is shown that for any maximal cyclically monotone operator the pointwise supremum of the sequence of fitzpatrick functions is closely related to rockafellar s antiderivative several examples are explicitly computed for the purpose of illustration in contrast to rockafellar s result a maximal 3 cyclically monotone operator need not be maximal monotone a simplified proof of asplund s observation that the rotation in the euclidean plane by n is n cyclically monotone but not n 1 cyclically monotone is provided the fitzpatrick family of the subdifferential operator of a sublinear and of an indicator function is studied in detail we conclude with a new proof of moreau s result concerning the convexity of the set of proximal mappings

linear equations modulo 2 and the l_1 diameter of convex bodies we design a randomized polynomial time algorithm which given a 3 tensor of real numbers a a ijk i j k 1 n such that for all i j k in 1 dots n we have a ijk a ikj a kji a jik a kij a jki and a iik a ijj a iji 0 computes a number operatorname alg a which satisfies with probability at least frac12 omega sqrt frac log n n t cdot max x in 1 1 n sum i j k 1 n a ijk x ix jx k le operatorname alg a le max x in 1 1 n sum i j k 1 n a ijk x ix jx k on the other hand we show via a simple reduction from a result of hastad and venkatesh random structures algorithms 25 2004 pp 117 149 that under the assumption np not subseteq dtime n log n o 1 for every epsilon 0 there is no algorithm that approximates max x in 1 1 n sum i j k 1 n a ijk x ix jx k within a factor of 2 log n 1 epsilon in time 2 log n o 1 our algorithm is based on a reduction to the problem of computing the diameter of a convex body in mathbb r n with respect to the l 1 norm we show that it is possible to do so up to a multiplicative error of o sqrt frac n log n while no randomized polynomial time algorithm can achieve accuracy o sqrt frac n log n this resolves a question posed by brieden et al in mathematika 48 2001 pp 63 105 we apply our new algorithm to improve the algorithm of hastad and venkatesh for the max e3 lin 2 problem given an overdetermined system mathcal e of n linear equations modulo 2 in n le n boolean variables such that in each equation only three distinct variables appear the goal is to approximate in polynomial time the maximum number of satisfiable equations in mathcal e minus frac n 2 i e we subtract the expected number of satisfied equations in a random assignment hastad and venkatesh obtained an algorithm which approximates this value up to a factor of o sqrt n we obtain an o sqrt frac n log n approximation algorithm by relating this problem to the refutation problem for random 3 cnf formulas we give evidence that obtaining a significant improvement over this approximation factor is likely to be difficult

a new condition for maximal monotonicity via representative functions abstract in this paper we give a weaker sufficient condition for the maximal monotonicity of the operator s a t a where s x x t y y are two maximal monotone operators a x y is a linear continuous mapping and x y are reflexive banach spaces we prove that our condition is weaker than the generalized interior point conditions given so far in the literature this condition is formulated using the representative functions of the operators involved in particular we rediscover some sufficient conditions given in the past using the so called fitzpatrick function for the maximal monotonicity of the sum of two maximal monotone operators and for the precomposition of a maximal monotone operator with a linear operator respectively

maximal monotonicity for the precomposition with a linear operator we give the weakest constraint qualification known to us that ensures the maximal monotonicity of the operator a circ t circ a when a is a linear continuous mapping between two reflexive banach spaces and t is a maximal monotone operator as a special case we get the weakest constraint qualification that guarantees the maximal monotonicity of the sum of two maximal monotone operators on a reflexive banach space then we give a weak constraint qualification assuring the bre zis haraux type approximation of the range of the subdifferential of the precomposition to a of a proper convex lower semicontinuous function in nonreflexive banach spaces extending and correcting in a special case an older result due to riahi

approximating the cut norm via grothendieck s inequality the cut norm a c of a real matrix a a ij i in r j in s is the maximum over all i subset r j subset s of the quantity sum i in i j in j a ij this concept plays a major role in the design of efficient approximation algorithms for dense graph and matrix problems here we show that the problem of approximating the cut norm of a given real matrix is max snp hard and we provide an efficient approximation algorithm this algorithm finds for a given matrix a a ij i in r j in s two subsets i subset r and j subset s such that sum i in i j in j a ij geq rho a c where rho 0 is an absolute constant satisfying rho 0 56 the algorithm combines semidefinite programming with a rounding technique based on grothendieck s inequality we present three known proofs of grothendieck s inequality with the necessary modifications which emphasize their algorithmic aspects these proofs contain rounding techniques which go beyond the random hyperplane rounding of goemans and williamson j acm 42 1995 pp 1115 1145 allowing us to transfer various algorithms for dense graph and matrix problems to the sparse case

a semidefinite relaxation scheme for multivariate quartic polynomial optimization with quadratic constraints we present a general semidefinite relaxation scheme for general n variate quartic polynomial optimization under homogeneous quadratic constraints unlike the existing sum of squares approach which relaxes the quartic optimization problems to a sequence of typically large linear semidefinite programs sdp our relaxation scheme leads to a possibly nonconvex quadratic optimization problem with linear constraints over the semidefinite matrix cone in mathbb r n times n it is shown that each alpha factor approximate solution of the relaxed quadratic sdp can be used to generate in randomized polynomial time an o alpha factor approximate solution for the original quartic optimization problem where the constant in o cdot depends only on problem dimension in the case where only one positive definite quadratic constraint is present in the quartic optimization problem we present a randomized polynomial time approximation algorithm which can provide a guaranteed relative approximation ratio of 1 o n 2

np hardness of deciding convexity of quartic polynomials and related problems we show that unless p np there exists no polynomial time or even pseudo polynomial time algorithm that can decide whether a multivariate polynomial of degree four or higher even degree is globally convex this solves a problem that has been open since 1992 when n z shor asked for the complexity of deciding convexity for quartic polynomials we also prove that deciding strict convexity strong convexity quasiconvexity and pseudoconvexity of polynomials of even degree four or higher is strongly np hard by contrast we show that quasiconvexity and pseudoconvexity of odd degree polynomials can be decided in polynomial time

biquadratic optimization over unit spheres and semidefinite programming relaxations this paper studies the so called biquadratic optimization over unit spheres min x in mathbb r n y in mathbb r m sum 1 leq i k leq n 1 leq j l leq m b ijkl x i y j x k y l subject to x 1 y 1 we show that this problem is np hard and there is no polynomial time algorithm returning a positive relative approximation bound then we present various approximation methods based on semidefinite programming sdp relaxations our theoretical results are as follows for general biquadratic forms we develop a frac 1 2 max m n 2 approximation algorithm under a slightly weaker approximation notion for biquadratic forms that are square free we give a relative approximation bound frac 1 nm when min n m is a constant we present two polynomial time approximation schemes ptass which are based on sum of squares sos relaxation hierarchy and grid sampling of the standard simplex for practical computational purposes we propose the first order sos relaxation a convex quadratic sdp relaxation and a simple minimum eigenvalue method and show their error bounds some illustrative numerical examples are also included

the ugc hardness threshold of the lp grothendieck problem for p 2 we consider the problem of given an n n matrix a aij whose diagonal entries vanish approximating in polynomial time the number rm opt p a max left sum i j 1 na ij x ix j x 1 ldots x n in mathbb r n wedge left sum i 1 n x i p right 1 p le 1 right when p 2 this is simply the problem of computing the maximum eigenvalue of a whereas for p actually it suffices to take p log n it is the grothendieck problem on the complete graph which was shown to have a o log n approximation algorithm in nemirovski et al nemirovski a c roos t terlaky 1999 on maximization of quadratic form over intersection of ellipsoids with common center math program ser a86 3 463 473 megretski megretski a 2001 relaxations of quadratic programs in operator theory and system analysis systems approximation singular integral operators and related topics bordeaux 2000 vol 129 operator theory advances and applications birkhauser basel 365 392 charikar and wirth charikar m a wirth 2004 maximizing quadratic programs extending grothendieck s inequality proc 45th annual sympos foundations comput sci ieee computer society 54 60 and was used in the work of charikar and wirth noted above to design the best known algorithm for the problem of computing the maximum correlation in correlation clustering thus the problem of approximating optp a interpolates between the spectral p 2 case and the correlation clustering p case from a physics point of view this problem corresponds to computing the ground states of spin glasses in a hard wall potential well we design a polynomial time algorithm which given p 2 and an n n matrix a aij with zeros on the diagonal computes optp a up to a factor p e 30 log p on the other hand assuming the unique games conjecture ugc we show that it is np hard to approximate optp a up to a factor smaller than p e hence as p the ugc hardness threshold for computing optp a is exactly p e 1 o 1

numerical solution of the optimal transportation problem using the monge ampere equation a numerical method for the solution of the elliptic monge ampere partial differential equation with boundary conditions corresponding to the optimal transportation ot problem is presented a local representation of the ot boundary conditions is combined with a finite difference scheme for the monge ampere equation email protected s method is implemented leading to a fast solver comparable to solving the laplace equation on the same grid several times theoretical justification for the method is given by a convergence proof in the companion paper 4 solutions are computed with densities supported on non convex and disconnected domains computational examples demonstrate robust performance on singular solutions and fast computational times

deterministic approximation algorithms for sphere constrained homogeneous polynomial optimization problems due to their fundamental nature and numerous applications sphere constrained polynomial optimization problems have received a lot of attention lately in this paper we consider three such problems i maximizing a homogeneous polynomial over the sphere ii maximizing a multilinear form over a cartesian product of spheres and iii maximizing a multiquadratic form over a cartesian product of spheres since these problems are generally intractable our focus is on designing polynomial time approximation algorithms for them by reducing the above problems to that of determining the l 2 diameters of certain convex bodies we show that they can all be approximated to within a factor of log n n d 2 1 deterministically where n is the number of variables and d is the degree of the polynomial this improves upon the currently best known approximation bound of 1 n d 2 1 in the literature we believe that our approach will find further applications in the design of approximation algorithms for polynomial optimization problems with provable guarantees

probability bounds for polynomial functions in random variables random sampling is a simple but powerful method in statistics and in the design of randomized algorithms in a typical application random sampling can be applied to estimate an extreme value say maximum of a function f over a set s n to do so one may select a simpler even finite subset s0 s randomly take some samples over s0 for a number of times and pick the best sample the hope is to find a good approximate solution with reasonable chance this paper sets out to present a number of scenarios for f s and s0 where certain probability bounds can be established leading to a quality assurance of the procedure in our setting f is a multivariate polynomial function we prove that if f is a d th order homogeneous polynomial in n variables and f is its corresponding super symmetric tensor and i i 1 2 n are i i d bernoulli random variables taking 1 or 1 with equal probability then prob f 1 2 n n d 2 f 1 where 0 are two universal constants and 1 denotes

approximation algorithms for homogeneous polynomial optimization with quadratic constraints in this paper we consider approximation algorithms for optimizing a generic multi variate homogeneous polynomial function subject to homogeneous quadratic constraints such optimization models have wide applications e g in signal processing magnetic resonance imaging mri data training approximation theory and portfolio selection since polynomial functions are non convex the problems under consideration are all np hard in general in this paper we shall focus on polynomial time approximation algorithms in particular we first study optimization of a multi linear tensor function over the cartesian product of spheres we shall propose approximation algorithms for such problem and derive worst case performance ratios which are shown to be dependent only on the dimensions of the model the methods are then extended to optimize a generic multi variate homogeneous polynomial function with spherical constraint likewise approximation algorithms are proposed with provable approximation performance ratios furthermore the constraint set is relaxed to be an intersection of co centered ellipsoids namely we consider maximization of a homogeneous polynomial over the intersection of ellipsoids centered at the origin and propose polynomial time approximation algorithms with provable worst case performance ratios numerical results are reported illustrating the effectiveness of the approximation algorithms studied