GPU accelerated variant of Schroeppel-Shamir's algorithm for solving the market split problem

TL;DR

This paper introduces a GPU-accelerated algorithm based on Schroeppel-Shamir's method to efficiently solve the market split problem, outperforming traditional solvers on benchmark instances.

Contribution

A novel hybrid CPU-GPU algorithm for MSP that exhaustively enumerates solutions and significantly improves solution times for larger instances.

Findings

Solved (9, 80) instances in less than 15 minutes

Solved (10, 90) instances in up to one day

Demonstrated efficiency over existing linear programming-based methods

Abstract

The market split problem (MSP), introduced by Cornuejols and Dawande (1998), is a challenging binary optimization problem that performs poorly on state-of-the-art linear programming-based branch-and-cut solvers. We present a novel algorithm for solving the feasibility version of this problem, derived from Schroeppel-Shamir's algorithm for the one-dimensional subset sum problem. Our approach is based on exhaustively enumerating one-dimensional solutions of MSP and utilizing GPUs to evaluate candidate solutions across the entire problem. The resulting hybrid CPU-GPU implementation efficiently solves instances with up to 10 constraints and 90 variables. We demonstrate the algorithm's performance on benchmark problems, solving instances of size (9, 80) in less than fifteen minutes and (10, 90) in up to one day.