Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization

TL;DR

This paper introduces a learning-based method that uses graph neural networks to predict parameters for approximation algorithms, improving solution quality for combinatorial optimization problems like TSP and k-cut.

Contribution

The paper presents a novel gradient estimation scheme for training GNNs to enhance traditional approximation algorithms in combinatorial optimization.

Findings

Achieves near-optimal solutions on TSP and k-cut problems.

Competitive with state-of-the-art learned solvers.

End-to-end training with a black-box gradient estimator.

Abstract

Combinatorial optimization (CO) problems arise across a broad spectrum of domains, including medicine, logistics, and manufacturing. While exact solutions are often computationally infeasible, many practical applications require high-quality solutions within a given time budget. To address this, we propose a learning-based approach that enhances existing non-learned approximation algorithms for CO. Specifically, we parameterize these approximation algorithms and train graph neural networks (GNNs) to predict parameter values that yield near-optimal solutions. Our method is trained end-to-end in a self-supervised fashion, using a novel gradient estimation scheme that treats the approximation algorithm as a black box. This approach combines the strengths of learning and traditional algorithms: the GNN learns from data to guide the algorithm toward better solutions, while the approximation algorithm ensures feasibility. We validate our method on two well-known combinatorial optimization problems: the travelling salesman problem (TSP) and the minimum k-cut problem. Our results demonstrate that the proposed approach is competitive with state-of-the-art learned CO solvers.