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.
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Kojima, M., Tunçel, L. Some Fundamental Properties of Successive Convex Relaxation Methods on LCP and Related Problems. Journal of Global Optimization 24, 333–348 (2002). https://doi.org/10.1023/A:1020300717650
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DOI: https://doi.org/10.1023/A:1020300717650