Relative-Interior Solution for the (Incomplete) Linear Assignment Problem with Applications to the Quadratic Assignment Problem

TL;DR

This paper introduces a linear-time method to compute a relative-interior solution for the dual linear assignment problem, improving bounds in quadratic assignment problem relaxations and outperforming commercial solvers in speed.

Contribution

It proposes a novel linear-time approach for finding relative-interior solutions in the dual LAP, enhancing dual-ascent algorithms for QAP bounds, especially for incomplete instances.

Findings

Method computes relative-interior solutions efficiently in linear time.

Approach provides bounds near the LP relaxation optimum.

Significantly faster than commercial LP solvers on benchmarks.

Abstract

We study the set of optimal solutions of the dual linear programming formulation of the linear assignment problem (LAP) to propose a method for computing a solution from the relative interior of this set. Assuming that an arbitrary dual-optimal solution and an optimal assignment are available (for which many efficient algorithms already exist), our method computes a relative-interior solution in linear time. Since the LAP occurs as a subproblem in the linear programming (LP) relaxation of the quadratic assignment problem (QAP), we employ our method as a new component in the family of dual-ascent algorithms that provide bounds on the optimal value of the QAP. To make our results applicable to the incomplete QAP, which is of interest in practical use-cases, we also provide a linear-time reduction from the incomplete LAP to the complete LAP along with a mapping that preserves optimality and membership in the relative interior. Our experiments on publicly available benchmarks indicate that our approach with relative-interior solution can frequently provide bounds near the optimum of the LP relaxation and its runtime is much lower when compared to a commercial LP solver.