Learning to Prune Instances of Steiner Tree Problem in Graphs

• Published in: arXiv.org
• Main Authors: Zhang, Jiwei, Ajwani, Deepak
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 09.10.2022
• Subjects: Combinatorial analysis; Graphs; Machine learning; Minimum weight; Nodes; Optimization

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.