  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.
