Maximum entropy estimation via Gauss-LP quadratures

Maxime Thély, Tobias Sutter, Peyman Mohajerin Esfahani, John Lygeros

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
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We present an approximation method to a class of parametric integration problems that naturally appear when solving the dual of the maximum entropy estimation problem. Our method builds up on a recent generalization of Gauss quadratures via an infinite-dimensional linear program, and utilizes a convex clustering algorithm to compute an approximate solution which requires reduced computational effort. It shows to be particularly appealing when looking at problems with unusual domains and in a multi-dimensional setting. As a proof of concept we apply our method to an example problem on the unit disc.

Original languageEnglish
Title of host publicationIFAC-PapersOnLine
Subtitle of host publicationProceedings 20th IFAC World Congress
EditorsDenis Dochain, Didier Henrion, Dimitri Peaucelle
Place of PublicationLaxenburg, Austria
Publication statusPublished - 2017
Event20th World Congress of the International Federation of Automatic Control (IFAC), 2017 - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
Conference number: 20

Publication series



Conference20th World Congress of the International Federation of Automatic Control (IFAC), 2017
Abbreviated titleIFAC 2017
Internet address


  • convex clustering
  • Entropy maximization
  • importance sampling
  • linear programming

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  • Cite this

    Thély, M., Sutter, T., Mohajerin Esfahani, P., & Lygeros, J. (2017). Maximum entropy estimation via Gauss-LP quadratures. In D. Dochain, D. Henrion, & D. Peaucelle (Eds.), IFAC-PapersOnLine: Proceedings 20th IFAC World Congress (Vol. 50-1, pp. 10470-10475). (IFAC-PapersOnLine; Vol. 50, No. 1). Elsevier.