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arXiv 2312.08074math.OCcs.AI
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PySCIPOpt-ML:将训练好的机器学习模型嵌入混合整数规划

PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs

Zuse Institute Berlin(柏林祖斯研究所);Technische Universität Berlin(柏林工业大学);Gurobi GmbH(古洛比有限公司)

Mark Turner, Antonia Chmiela, Thorsten Koch, Michael Winkler

AI总结:

本文提出PySCIPOpt-ML,一个开源工具,可自动将训练好的机器学习模型嵌入混合整数规划,并引入SurrogateLIB库及实验结果,以简化ML约束的集成。

摘要:

混合整数规划(MIP)是建模现实世界优化问题的标准工具。然而,对于许多此类问题,变量之间关系的信息要么不完整,要么高度复杂,使得直接建模变得困难甚至不可能。为了克服这些障碍,机器学习(ML)预测器常被用来表示这些关系,并作为代理模型嵌入到MIP中。由于可用的ML框架数量众多且许多ML预测器结构复杂,将这些预测器转化为MIP是一项非常棘手的任务。在本文中,我们介绍了PySCIPOpt-ML,一个用于自动将训练好的ML预测器制定并嵌入到MIP中的开源工具。通过直接与一系列常用的ML框架以及一个开源MIP求解器接口,PySCIPOpt-ML提供了一种将ML约束轻松集成到优化问题中的途径。除PySCIPOpt-ML外,我们还引入了SurrogateLIB,一个包含嵌入ML约束的MIP实例的库,并展示了在SurrogateLIB上的计算结果,为实际可嵌入的ML预测器规模提供了直观认识。该项目可在https://github.com/Opt-Mucca/PySCIPOpt-ML获取。

英文摘要:

A standard tool for modelling real-world optimisation problems is mixed-integer programming (MIP). However, for many of these problems, information about the relationships between variables is either incomplete or highly complex, making it difficult or even impossible to model the problem directly. To overcome these hurdles, machine learning (ML) predictors are often used to represent these relationships and are then embedded in the MIP as surrogate models. Due to the large amount of available ML frameworks and the complexity of many ML predictors, formulating such predictors into MIPs is a highly non-trivial task. In this paper, we introduce PySCIPOpt-ML, an open-source tool for the automatic formulation and embedding of trained ML predictors into MIPs. By directly interfacing with a broad range of commonly used ML frameworks and an open-source MIP solver, PySCIPOpt-ML provides a way to easily integrate ML constraints into optimisation problems. Alongside PySCIPOpt-ML, we introduce, SurrogateLIB, a library of MIP instances with embedded ML constraints, and present computational results over SurrogateLIB, providing intuition on the scale of ML predictors that can be practically embedded. The project is available at https://github.com/Opt-Mucca/PySCIPOpt-ML.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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