A Classifier to Decide on the Linearization of Mixed-Integer Quadratic Problems in CPLEX

Pierre Bonami (), Andrea Lodi () and Giulia Zarpellon ()
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Pierre Bonami: Gurobi Optimization, Madrid 28006, Spain
Andrea Lodi: Canada Excellence Research Chair “Data Science for Real-time Decision-Making” Polytechnique Montréal, Montreal, Quebec H3C 3A7, Canada; Jacobs Technion-Cornell Institute, Cornell Tech and Technion, Israel Institute of Technology, New York, New York 10024
Giulia Zarpellon: Vector Institute, Toronto, Ontario M5G 1M1, Canada

Operations Research, 2022, vol. 70, issue 6, 3303-3320

Abstract: With the aim of fully embedding learned predictions in the algorithmic design of a mixed-integer quadratic programming (MIQP) solver, we translate the algorithmic question of whether to linearize convex MIQPs into a classification task and use machine learning (ML) techniques to tackle it. We represent MIQPs and the linearization decision by careful target and feature engineering. Computational experiments and evaluation metrics are designed to further incorporate the optimization knowledge in the learning pipeline. As a practical result, a classifier deciding on MIQP linearization is successfully deployed in CPLEX 12.10.0: to the best of our knowledge, we establish the first example of an end-to-end integration of ML into a commercial optimization solver and ultimately contribute a general-purpose methodology for combining ML-based decisions and mixed-integer programming technology.

Keywords: Machine Learning and Data Science; mixed-integer quadratic programming; machine learning; classification; linearization (search for similar items in EconPapers)
Date: 2022
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