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

被引：0

作者：

Masakazu Kojima

Levent Tunçel

机构：

[1] Tokyo Institute of Technology,Department of Mathematical and Computing Sciences

[2] University of Waterloo,Department of Combinatorics and Optimization, Faculty of Mathematics

来源：

Journal of Global Optimization | 2002年 / 24卷

关键词：

Nonconvex quadratic optimization; Linear complementarity problem; Semidefinite programming; Global optimization; SDP relaxation; Convex relaxation; Lift-and-project procedures;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：333 / 348

页数：15