Learning-Assisted Relaxations and Approximations

Gonzalo E. Constante-Flores and Antonio J. Conejo
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Gonzalo E. Constante-Flores: Purdue University
Antonio J. Conejo: The Ohio State University

Chapter Chapter 4 in Optimization via Relaxation and Decomposition, 2025, pp 73-103 from Springer

Abstract: Abstract This chapter describes applications of machine learning techniques, particularly supervised learning, to assist the solution of optimization problems, including warm-starting optimization solvers, identifying active/inactive inequality constraints and subsets of invariant variables, linearizing and convexifying nonconvex equations, and using machine learning models as surrogates of complex/unknown sets of equations.

Keywords: Solution initialization; Constraint simplification; Variable reduction; Machine learning for optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-87405-5_4

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