Automated Discovery of Branching Rules with Optimal Complexity for the Maximum Independent Set Problem

TL;DR

This paper presents an automated method to discover optimal branching rules for the maximum independent set problem, significantly improving algorithm efficiency and reducing search complexity compared to human-designed rules.

Contribution

It introduces an algorithm that automatically generates optimal branching rules for sub-graphs, enabling on-the-fly rule generation with provable optimality.

Findings

Achieves an average complexity of O(1.0441^n) on 3-regular graphs

Reduces the number of branches compared to existing methods

Demonstrates the effectiveness of automated rule discovery in combinatorial optimization

Abstract

The branching algorithm is a fundamental technique for designing fast exponential-time algorithms to solve combinatorial optimization problems exactly. It divides the entire solution space into independent search branches using predetermined branching rules, and ignores the search on suboptimal branches to reduce the time complexity. The complexity of a branching algorithm is primarily determined by the branching rules it employs, which are often designed by human experts. In this paper, we show how to automate this process with a focus on the maximum independent set problem. The main contribution is an algorithm that efficiently generate optimal branching rules for a given sub-graph with tens of vertices. Its efficiency enables us to generate the branching rules on-the-fly, which is provably optimal and significantly reduces the number of branches compared to existing methods that rely on expert-designed branching rules. Numerical experiment on 3-regular graphs shows an average complexity of O(1.0441^n) can be achieved, better than any previous methods.