In this paper, we present improved versions of the standard semidefinite relaxation for quadratic programming, that underlies many important results in robustness analysis and combinatorial optimization. It is shown that the proposed polynomial time convex conditions are at least as strong as the standard ones, and usually better, but at a higher computational cost. Several applications of the ...

(ABSTRACT) Despite recent advances in convex optimization techniques, the areas of discrete and continuous nonconvex optimization remain formidable, particularly when globally optimal solutions are desired. Most solution techniques, such as branch-and-bound, are enumerative in nature, and the rate of their convergence is strongly dependent on the accuracy of the bounds provided, and therefore, ...

These notes give an introduction to duality theory in the context of linear and positive semidefinite programming. These notes are based on material from Convex Analysis and Nonlinear Optimization by Borwein and Lewis and Numerical Optimization by Nocedal and Wright. Two examples are given to show how duality can be used. The first optimization application is to find the matrix in an affine fam...

The standard quadratic program (QPS) is minx∈∆ xT Qx, where ∆ ⊂ <n is the simplex ∆ = {x ≥ 0 | ni=1 xi = 1}. QPS can be used to formulate combinatorial problems such as the maximum stable set problem, and also arises in global optimization algorithms for general quadratic programming when the search space is partitioned using simplices. One class of “d.c.” (for “difference between convex”) boun...

During the last years, kernel based methods proved to be very successful for many real-world learning problems. One of the main reasons for this success is the efficiency on large data sets which is a result of the fact that kernel methods like Support Vector Machines (SVM) are based on a convex optimization problem. Solving a new learning problem can now often be reduced to the choice of an ap...

We propose a modified alternate direction method for solving convex quadratically constrained quadratic semidefinite optimization problems. The method is a first-order method, therefore requires much less computational effort per iteration than the second-order approaches such as the interior point methods or the smoothing Newton methods. In fact, only a single inexact metric projection onto th...

A disadvantage of the SDP (semidefinite programming) relaxation method for quadratic and/or combinatorial optimization problems lies in its expensive computational cost. This paper proposes a SOCP (second-order-cone programming) relaxation method, which strengthens the lift-and-project LP (linear programming) relaxation method by adding convex quadratic valid inequalities for the positive semid...

In this we paper we study techniques for generating valid convex constraints for mixed 0-1 conic programs. We show that many of the techniques developed for generating linear cuts for mixed 0-1 linear programs, such as the Gomory cuts, the lift-and-project cuts, and cuts from other hierarchies of tighter relaxations, extend in a straightforward manner to mixed 0-1 conic programs. We also show t...

A small polygon is a convex polygon of unit diameter. We are interested in small polygons which have the largest area for a given number of vertices n. Many instances are already solved in the literature, namely for all odd n, and for n = 4, 6 and 8. Thus, for even n ≥ 10, instances of this problem remain open. Finding those largest small polygons can be formulated as nonconvex quadratic progra...