EconPapers    
Economics at your fingertips  
 

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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037722172030758X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

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

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2022-01-25
Handle: RePEc:eee:ejores:v:290:y:2021:i:3:p:807-828