View in EDS HTML Full Text PDF Full Text

Learning to Optimize Contextually Constrained Problems for Real-Time Decision Generation.

Saved in:
Bibliographic Details
Authors: Babier, Aaron1 (AUTHOR) ababier@mie.utoronto.ca, Chan, Timothy C. Y.1 (AUTHOR) tcychan@mie.utoronto.ca, Diamant, Adam2 (AUTHOR) adiamant@schulich.yorku.ca, Mahmood, Rafid3 (AUTHOR) mahmood@telfer.uottawa.ca
Source: Management Science (INFORMS). Feb2025, Vol. 71 Issue 2, p1165-1186. 22p.
Subject Terms: *Statistical decision making, *Operations research, Interior-point methods, Supervised learning, Deep learning
Abstract: The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities. In this paper, we combine ideas from both fields to address the problem of learning to generate decisions to instances of optimization problems with potentially nonlinear or nonconvex constraints where the feasible set varies with contextual features. We propose a novel framework for training a generative model to produce provably optimal decisions by combining interior point methods and adversarial learning, which we further embed within an iterative data generation algorithm. To this end, we first train a classifier to learn feasibility and then train the generative model to produce optimal decisions to an optimization problem using the classifier as a regularizer. We prove that decisions generated by our model satisfy in-sample and out-of-sample optimality guarantees. Furthermore, the learning models are embedded in an active learning loop in which synthetic instances are iteratively added to the training data; this allows us to progressively generate provably tighter optimal decisions. We investigate case studies in portfolio optimization and personalized treatment design, demonstrating that our approach yields advantages over predict-then-optimize and supervised deep learning techniques, respectively. In particular, our framework is more robust to parameter estimation error compared with the predict-then-optimize paradigm and can better adapt to domain shift as compared with supervised learning models. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported in part by the Natural Sciences and Engineering Research Council of Canada. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.03565. [ABSTRACT FROM AUTHOR]
Database: Entrepreneurial Studies Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: ent
DbLabel: Entrepreneurial Studies Source
An: 182990733
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Babier%2C+Aaron%22">Babier, Aaron</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ababier@mie.utoronto.ca</i><br /><searchLink fieldCode="AR" term="%22Chan%2C+Timothy+C%2E+Y%2E%22">Chan, Timothy C. Y.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tcychan@mie.utoronto.ca</i><br /><searchLink fieldCode="AR" term="%22Diamant%2C+Adam%22">Diamant, Adam</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> adiamant@schulich.yorku.ca</i><br /><searchLink fieldCode="AR" term="%22Mahmood%2C+Rafid%22">Mahmood, Rafid</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> mahmood@telfer.uottawa.ca</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Management+Science+%28INFORMS%29%22">Management Science (INFORMS)</searchLink>. Feb2025, Vol. 71 Issue 2, p1165-1186. 22p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Statistical+decision+making%22">Statistical decision making</searchLink><br />*<searchLink fieldCode="DE" term="%22Operations+research%22">Operations research</searchLink><br /><searchLink fieldCode="DE" term="%22Interior-point+methods%22">Interior-point methods</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities. In this paper, we combine ideas from both fields to address the problem of learning to generate decisions to instances of optimization problems with potentially nonlinear or nonconvex constraints where the feasible set varies with contextual features. We propose a novel framework for training a generative model to produce provably optimal decisions by combining interior point methods and adversarial learning, which we further embed within an iterative data generation algorithm. To this end, we first train a classifier to learn feasibility and then train the generative model to produce optimal decisions to an optimization problem using the classifier as a regularizer. We prove that decisions generated by our model satisfy in-sample and out-of-sample optimality guarantees. Furthermore, the learning models are embedded in an active learning loop in which synthetic instances are iteratively added to the training data; this allows us to progressively generate provably tighter optimal decisions. We investigate case studies in portfolio optimization and personalized treatment design, demonstrating that our approach yields advantages over predict-then-optimize and supervised deep learning techniques, respectively. In particular, our framework is more robust to parameter estimation error compared with the predict-then-optimize paradigm and can better adapt to domain shift as compared with supervised learning models. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported in part by the Natural Sciences and Engineering Research Council of Canada. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.03565. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ent&AN=182990733
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1287/mnsc.2020.03565
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 1165
    Subjects:
      – SubjectFull: Statistical decision making
        Type: general
      – SubjectFull: Operations research
        Type: general
      – SubjectFull: Interior-point methods
        Type: general
      – SubjectFull: Supervised learning
        Type: general
      – SubjectFull: Deep learning
        Type: general
    Titles:
      – TitleFull: Learning to Optimize Contextually Constrained Problems for Real-Time Decision Generation.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Babier, Aaron
      – PersonEntity:
          Name:
            NameFull: Chan, Timothy C. Y.
      – PersonEntity:
          Name:
            NameFull: Diamant, Adam
      – PersonEntity:
          Name:
            NameFull: Mahmood, Rafid
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 00251909
          Numbering:
            – Type: volume
              Value: 71
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Management Science (INFORMS)
              Type: main
ResultId 1