Kernel-function-based primal-dual interior-point methods for convex quadratic optimization over symmetric cone
TL;DR: In this article, the complexity analysis of kernel-function-based primal-dual interior-point methods for convex quadratic optimization over a symmetric cone is presented, and the currently best known iteration bounds for large and small-update methods are derived.
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Abstract: In this paper, we give a unified computational scheme for the complexity analysis of kernel-function-based primal-dual interior-point methods for convex quadratic optimization over symmetric cone. By using Euclidean Jordan algebras, the currently best-known iteration bounds for large- and small-update methods are derived, namely, and , respectively. Furthermore, this unifies the analysis for a wide class of conic optimization problems. MSC:90C25, 90C51.
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