Some fundamental properties of successive convex relaxation methods on LCP and related problems
Optimization and Control
2007-05-23 v1 Metric Geometry
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 which is not very complicated. 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.
Keywords
Cite
@article{arxiv.math/9905199,
title = {Some fundamental properties of successive convex relaxation methods on LCP and related problems},
author = {Masakazu Kojima and Levent Tuncel},
journal= {arXiv preprint arXiv:math/9905199},
year = {2007}
}