Learning to Prune Instances of Steiner Tree Problem in Graphs

TL;DR

This paper applies a machine learning-based pruning framework to the NP-hard Steiner tree problem, achieving near-optimal solutions faster than traditional ILP solvers, demonstrating the framework's broad potential.

Contribution

It introduces the application of the learning-to-prune framework to the Steiner tree problem, showing significant efficiency gains over conventional methods.

Findings

Near-optimal solutions are computed faster than ILP solvers.

The learning-to-prune framework is effective for complex combinatorial problems.

Potential for broad application in combinatorial optimization.

Abstract

We consider the Steiner tree problem on graphs where we are given a set of nodes and the goal is to find a tree sub-graph of minimum weight that contains all nodes in the given set, potentially including additional nodes. This is a classical NP-hard combinatorial optimisation problem. In recent years, a machine learning framework called learning-to-prune has been successfully used for solving a diverse range of combinatorial optimisation problems. In this paper, we use this learning framework on the Steiner tree problem and show that even on this problem, the learning-to-prune framework results in computing near-optimal solutions at a fraction of the time required by commercial ILP solvers. Our results underscore the potential of the learning-to-prune framework in solving various combinatorial optimisation problems.