Print Email Facebook Twitter Maximum entropy estimation via Gauss-LP quadratures Title Maximum entropy estimation via Gauss-LP quadratures Author Thély, Maxime (ETH Zürich) Sutter, Tobias (ETH Zürich) Mohajerin Esfahani, P. (TU Delft Team Tamas Keviczky) Lygeros, John (ETH Zürich) Contributor Dochain, Denis (editor) Henrion, Didier (editor) Peaucelle, Dimitri (editor) Date 2017 Abstract 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. Subject convex clusteringEntropy maximizationimportance samplinglinear programming To reference this document use: http://resolver.tudelft.nl/uuid:64b55308-282f-4475-bb18-fe545b6936fc DOI https://doi.org/10.1016/j.ifacol.2017.08.1977 Publisher Elsevier, Laxenburg, Austria Source IFAC-PapersOnLine: Proceedings 20th IFAC World Congress, 50-1 Event 20th World Congress of the International Federation of Automatic Control (IFAC), 2017, 2017-07-09 → 2017-07-14, Toulouse, France Series IFAC-PapersOnLine, 50 (1) Part of collection Institutional Repository Document type conference paper Rights © 2017 Maxime Thély, Tobias Sutter, P. Mohajerin Esfahani, John Lygeros Files PDF 1_s2.0_S2405896317326149_main.pdf 801.96 KB Close viewer /islandora/object/uuid:64b55308-282f-4475-bb18-fe545b6936fc/datastream/OBJ/view