A Relaxation and Rectification (ReCR) Framework for Systems with Linear and Complementary Constraints: Theoretical Foundation, Algorithms and Numerical Experiments
Systems defined by linear and complementarity constraints (SLCCs) arise frequently in engineering, economics, and other related fields. They also appear in the optimality conditions of many challenging optimization models, such as bilinear optimization and linearly constrained quadratic optimization. It is known that finding a feasible solution to an SLCC is NP-hard in general. In this paper, we study the feasibility problem for a given SLCC: either find a feasible solution or determine that the system is infeasible. To this end, we introduce a universal relaxation theory (URT), which reformulates SLCC feasibility as an equivalent bilinear optimization problem with linear constraints in a lifted space. We then analyze the resulting bilinear model and derive necessary and sufficient optimality conditions for its global solutions. Based on these theoretical insights, we introduce a relaxation-rectification (ReCR) framework for finding a feasible solution to a given SLCC instance or certifying infeasibility. We develop several ReCR methods that differ in their working spaces and subproblem formulations and analyze their convergence properties. We also develop a numerical procedure for obtaining an infeasibility certificate when the ReCR methods do not find a feasible solution. We conduct numerical experiments to evaluate the reliability, robustness, and scalability of the proposed ReCR methods and compare them with existing SLCC solvers. On the tested small- and large-scale LCP instances from the literature, the proposed ReCR methods typically find feasible solutions in a few iterations. We also extend the benchmark with more challenging medium-scale SLCC instances, on which the proposed hybrid ReCR (H-ReCR) method exhibits promising performance.