Learning Pseudo-Backdoors for Mixed Integer Programs

TL;DR

This paper introduces a machine learning method to identify pseudo-backdoors in Mixed Integer Programs, enabling faster solutions by prioritizing decision variables, and demonstrates improved performance over traditional solvers on specific problem instances.

Contribution

The paper presents a data-driven approach to predict pseudo-backdoors in MIPs, enhancing solver efficiency and providing a novel way to leverage problem structure for optimization.

Findings

Efficiently identifies high-quality pseudo-backdoors in MIPs.

Improves solution times compared to Gurobi on tested instances.

Applicable to generalized independent set problems.

Abstract

We propose a machine learning approach for quickly solving Mixed Integer Programs (MIP) by learning to prioritize a set of decision variables, which we call pseudo-backdoors, for branching that results in faster solution times. Learning-based approaches have seen success in the area of solving combinatorial optimization problems by being able to flexibly leverage common structures in a given distribution of problems. Our approach takes inspiration from the concept of strong backdoors, which corresponds to a small set of variables such that only branching on these variables yields an optimal integral solution and a proof of optimality. Our notion of pseudo-backdoors corresponds to a small set of variables such that only branching on them leads to faster solve time (which can be solver dependent). A key advantage of pseudo-backdoors over strong backdoors is that they are much amenable to data-driven identification or prediction. Our proposed method learns to estimate the solver performance of a proposed pseudo-backdoor, using a labeled dataset collected on a set of training MIP instances. This model can then be used to identify high-quality pseudo-backdoors on new MIP instances from the same distribution. We evaluate our method on the generalized independent set problems and find that our approach can efficiently identify high-quality pseudo-backdoors. In addition, we compare our learned approach against Gurobi, a state-of-the-art MIP solver, demonstrating that our method can be used to improve solver performance.