PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs
CoRR(2023)
摘要
A standard tool for modelling real-world optimisation problems is
mixed-integer programming (MIP). However, for many of these problems there is
either incomplete information describing variable relations, or the relations
between variables are highly complex. To overcome both these hurdles, machine
learning (ML) models are often used and embedded in the MIP as surrogate models
to represent these relations. Due to the large amount of available ML
frameworks, formulating ML models into MIPs is highly non-trivial. In this
paper we propose a tool for the automatic MIP formulation of trained ML models,
allowing easy integration of ML constraints into MIPs. In addition, we
introduce a library of MIP instances with embedded ML constraints. The project
is available at https://github.com/Opt-Mucca/PySCIPOpt-ML.
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