Linear Phase Transition in Random Linear Constraint Satisfaction Problem

Abstract

Our model is a generalized linear programming relaxation of a much studied random K-SAT problem. Specifically, a set of linear constraints C on K variables is fixed. From a pool of n variables, K variables are chosen uniformly at random and a constraint is chosen from C also uniformly at random. This procedure is repeated m times independently. We ask the following question: is the resulting linear programming problem feasible? We show that the feasibility property experiences a linear phase transition, when n diverges to infinity and m=cn for some constant c. Namely, there exists a critical value c* such that, when c<c*, the system is feasible or is asymptotically almost feasible, as n increases, but, when c>c*, the &#34;distance&#34; from feasibility is at least a positive constant independent of n. Our results are obtained using powerful local weak convergence methods developed by Aldous and Steele. By exploiting a linear programming duality, our theorem implies the following result in the context of sparse random graphs G(n, cn) on n nodes with cn edges, where edges are equipped with randomly generated weights. Let M(n,c) denote maximum weight matching in G(n, cn). We prove that when c is a constant and n\to\infty, the limit \lim_n M(n,c)/n exists, with high probability. We further extend this result to maximum weight b-matchings also in G(n,cn).