Semidefinite programming

Semidefinite programming (SDP) is a subfield of convex optimization concerned with the optimization of a linear objective function (a user-specified function that the user wants to minimize or maximize) over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron. Semidefinite programming is a relatively new field of optimization which is of growing interest for several reasons. Many practical problems in operations research and combinatorial optimization can be modeled or approximated as semidefinite programming problems. In automatic control theory, SDPs are used in the context of linear matrix inequalities. SDPs are in fact a special case of cone programming and can be efficiently solved by interior point methods. All linear programs and (convex) quadratic programs can be expressed as SDPs, and via hierarchies of SDPs the solutions of polynomial optimization problems can be approximated. Semidefinite programming has been used in

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Some constructions of maximal witness codes

Nikolaos Makriyannis, Bertrand Meyer

Given a code C is an element of F-2(n) and a word c is an element of C, a witness of c is a subset W subset of {, 1 ... , n} of coordinate positions such that c differs from any other codeword c' is an element of C on the indices in W. If any codeword posseses a witness of given length w, C is called a w-witness code. This paper gives new constructions of large w-witness codes and proves with a numerical method that their sizes are maximal for certain values of n and w. Our technique is in the spirit of Delsarte's linear programming bound on the size of classical codes and relies on the Lovasz theta number, semidefinite programming, and reduction through symmetry.

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

A convex set of robust D—stabilizing controllers using Cauchy's argument principle

Alireza Karimi, Philippe Louis Schuchert

A new approach is presented to obtain a convex set of robust D—stabilizing fixed structure controllers, relying on Cauchy's argument principle. A convex set of D—stabilizing controllers around an initial D—stabilizing controller for a multi-model set is represented by an infinite set of Linear Matrix Inequalities (LMIs). By appropriate sampling of the D—stability boundary, a Semi-Definite Programming (SDP) is proposed that can be integrated in other synthesis approaches to ensure D—stability along other design specifications. To showcase utility of the proposed approach, two different examples are given: a boost converter with multi-model uncertainty and a laser-beam system modeled by an identified finite impulse response.

Elsevier2022

A primal-dual semidefinite programming approach to stochastic linear quadratic control problems

Nathalie Pellet

The purpose of this article (...) is to derive an algorithm for solving stochastic linear quadratic control problems over infinite time horizon using a primal-dual semidefinite programming approach.

2009

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Linear programming

Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternative

Convex optimization

Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently, maximizing concave functions over convex sets