Optimization problems for machine learning: A survey

Claudio Gambella, Bissan Ghaddar and Joe Naoum-Sawaya

European Journal of Operational Research, 2021, vol. 290, issue 3, 807-828

Abstract: This paper surveys the machine learning literature and presents in an optimization framework several commonly used machine learning approaches. Particularly, mathematical optimization models are presented for regression, classification, clustering, deep learning, and adversarial learning, as well as new emerging applications in machine teaching, empirical model learning, and Bayesian network structure learning. Such models can benefit from the advancement of numerical optimization techniques which have already played a distinctive role in several machine learning settings. The strengths and the shortcomings of these models are discussed and potential research directions and open problems are highlighted.

Keywords: Analytics; Mathematical programming; Machine learning; Deep learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:290:y:2021:i:3:p:807-828

DOI: 10.1016/j.ejor.2020.08.045

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