用大模型一键配置优化求解器参数，少数据快生效。
LLMs for Cold-Start Cutting Plane Separator Configuration
- 用大模型分析问题描述和求解器信息，自动生成切割平面配置。
- 仅需少量实例求解，性能媲美甚至超越现有方法。
- 无需定制接口，秒级生成配置，适合非专家用户快速上手。
混合整数线性规划（MILP）求解器包含数百个参数，对性能影响巨大，但对非专家用户极难配置。现有机器学习方法需上千个相关实例训练，泛化能力差，且难以集成到现有求解流程中。本文提出一种基于大语言模型（LLM）的框架，利用问题描述和求解器特定的切割平面摘要来配置分离器。为降低LLM输出波动，引入聚类与聚合的集成策略，将候选配置压缩为少数高性能组合。本方法无需定制求解器接口，通过简单API调用在秒级生成配置，仅需求解少量实例。在标准合成与真实世界MILP问题上的大量实验表明，该方法以远少于现有方法的数据与计算开销，达到或超越当前最优配置效果。
原文摘要 · Abstract (English)
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.
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