Convexity Properties Associated with Nonconvex Quadratic Matrix Functions and Applications to Quadratic Programming
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We establish several convexity results which are concerned with nonconvex quadratic matrix (QM) functions: strong duality of quadratic matrix programming problems, convexity of the image of mappings comprised of several QM functions and existence of a corresponding S-lemma. As a consequence of our results, we prove that a class of quadratic problems involving several functions with similar matrix terms has a zero duality gap. We present applications to robust optimization, to solution of linear systems immune to implementation errors and to the problem of computing the Chebyshev center of an intersection of balls.
KeywordsQuadratic matrix functions Strong duality Extended S-lemma Semidefinite relaxation Convexity of quadratic maps
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