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Some Fundamental Properties of Successive Convex Relaxation Methods on LCP and Related Problems

By Masakazu Kojima and Levent Tunçel

Abstract

Abstract: General Successive Convex Relaxation Methods (SRCMs) can be used to compute the convex hull of any compact set, in an Euclidean space, described by a system of quadratic inequalities and a compact convex set. Linear Complementarity Problems (LCPs) make an interesting and rich class of structured nonconvex optimization problems. In this paper, we study a few of the specialized lift-and-project methods and some of the possible ways of applying the general SCRMs to LCPs and related problems

Topics: Nonconvex Quadratic Optimization, Linear Complementarity Problem, Semidefinite Programming, Global Optimization, SDP Relaxation, Convex Relaxation, Lift-and- Project Procedures
Year: 1999
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.9086
Provided by: CiteSeerX
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