Fast Solutions to Projective Monotone Linear Complementarity Problems

TL;DR

This paper introduces an efficient interior-point algorithm for solving a special class of monotone linear complementarity problems with a structured matrix, achieving faster per-iteration complexity when the matrix's rank is low.

Contribution

The paper presents a novel interior-point potential-reduction algorithm tailored for projective LCPs, reducing computational complexity per iteration compared to general methods.

Findings

Algorithm solves projective LCPs in $O(rac{1}{ oot n} \, \ln(1/\epsilon))$ iterations.

Each iteration requires $O(nk^2)$ flops, significantly less than general methods for low-rank cases.

Method works despite solutions not being confined to low-rank subspaces.

Abstract

We present a new interior-point potential-reduction algorithm for solving monotone linear complementarity problems (LCPs) that have a particular special structure: their matrix $M\in{\mathbb R}^{n\times n}$ can be decomposed as $M=\Phi U + \Pi_0$, where the rank of $\Phi$ is $k<n$, and $\Pi_0$ denotes Euclidean projection onto the nullspace of $\Phi^\top$. We call such LCPs projective. Our algorithm solves a monotone projective LCP to relative accuracy $\epsilon$ in $O(\sqrt n \ln(1/\epsilon))$ iterations, with each iteration requiring $O(nk^2)$ flops. This complexity compares favorably with interior-point algorithms for general monotone LCPs: these algorithms also require $O(\sqrt n \ln(1/\epsilon))$ iterations, but each iteration needs to solve an $n\times n$ system of linear equations, a much higher cost than our algorithm when $k\ll n$. Our algorithm works even though the solution to a projective LCP is not restricted to lie in any low-rank subspace.