GFORS: GPU-Accelerated First-Order Method with Randomized Sampling for Binary Integer Programs

TL;DR

GFORS is a GPU-accelerated framework for binary integer programming that combines a first-order method with randomized sampling, achieving high-quality solutions efficiently on large-scale problems.

Contribution

Introduces GFORS, a novel GPU-native framework that integrates a first-order routine with randomized sampling for scalable binary integer programming.

Findings

GFORS provides high-quality solutions within seconds on large instances.

On small-medium instances, GFORS is faster but less optimal than state-of-the-art solvers.

GFORS scales well and offers competitive solution quality under time constraints.

Abstract

We present GFORS, a GPU-accelerated framework for large binary integer programs. It couples a first-order (PDHG-style) routine that guides the search in the continuous relaxation with a randomized, feasibility-aware sampling module that generates batched binary candidates. Both components are designed to run end-to-end on GPUs with minimal CPU-GPU synchronization. The framework establishes near-stationary-point guarantees for the first-order routine and probabilistic bounds on the feasibility and quality of sampled solutions, while not providing global optimality certificates. To improve sampling effectiveness, we introduce techniques such as total-unimodular reformulation, customized sampling design, and monotone relaxation. On classic benchmarks (set cover, knapsack, max cut, 3D assignment, facility location), baseline state-of-the-art exact solvers remain stronger on small-medium instances, while GFORS attains high-quality incumbents within seconds; on large instances, GFORS yields substantially shorter runtimes, with solution quality often comparable to -- or better than -- the baseline under the same time limit. These results suggest that GFORS can complement exact solvers by delivering scalable, GPU-native search when problem size and response time are the primary constraints.