LLMs for Cold-Start Cutting Plane Separator Configuration
This addresses the challenge of parameter configuration in MILP solvers for users without expert knowledge, though it is incremental as it builds on existing ML and LLM techniques.
The paper tackles the problem of configuring cutting plane separators in mixed integer linear programming (MILP) solvers, which is difficult for non-experts, by proposing an LLM-based framework that uses problem descriptions and solver summaries. The result shows that this approach matches or outperforms state-of-the-art methods with significantly less data and computation.
Mixed integer linear programming (MILP) solvers expose hundreds of parameters that have an outsized impact on performance but are difficult to configure for all but expert users. Existing machine learning (ML) approaches require training on thousands of related instances, generalize poorly and can be difficult to integrate into existing solver workflows. We propose a large language model (LLM)-based framework that configures cutting plane separators using problem descriptions and solver-specific separator summaries. To reduce variance in LLM outputs, we introduce an ensembling strategy that clusters and aggregates candidate configurations into a small portfolio of high-performing configurations. Our method requires no custom solver interface, generates configurations in seconds via simple API calls, and requires solving only a small number of instances. Extensive experiments on standard synthetic and real-world MILPs show our approach matches or outperforms state-of-the-art configuration methods with a fraction of the data and computation.