Application of Causal Inference Techniques to the Maximum Weight Independent Set Problem

TL;DR

This paper introduces novel causal inference techniques for the maximum weight independent set problem, significantly reducing search space and improving solution quality through mathematical properties and reduction algorithms.

Contribution

It proposes two extension theorems and two causal inference techniques that enhance reduction algorithms for MWIS, leading to smaller graphs and better solutions.

Findings

Remaining graph size reduced by over 32.6%

Number of solvable instances increased from 1 to 5

Improved exact and heuristic algorithm performance

Abstract

A powerful technique for solving combinatorial optimization problems is to reduce the search space without compromising the solution quality by exploring intrinsic mathematical properties of the problems. For the maximum weight independent set (MWIS) problem, using an upper bound lemma which says the weight of any independent set not contained in the MWIS is bounded from above by the weight of the intersection of its closed neighbor set and the MWIS, we give two extension theorems -- independent set extension theorem and vertex cover extension theorem. With them at our disposal, two types of causal inference techniques (CITs) are proposed on the assumption that a vertex is strongly reducible (included or not included in all MWISs) or reducible (contained or not contained in a MWIS). One is a strongly reducible state-preserving technique, which extends a strongly reducible vertex into a vertex set where all vertices have the same strong reducibility. The other, as a reducible state-preserving technique, extends a reducible vertex into a vertex set with the same reducibility as that vertex and creates some weighted packing constraints to narrow the search space. Numerical experiments show that our CITs can help reduction algorithms find much smaller remaining graphs, improve the ability of exact algorithms to find the optimal solutions and help heuristic algorithms produce approximate solutions of better quality. In particular, detailed tests on $12$ representative graphs generated from datasets in Network Data Repository demonstrate that, compared to the state-of-the-art algorithms, the size of remaining graphs is further reduced by more than 32.6%, and the number of solvable instances is increased from 1 to 5.