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.
Original language | English |
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Title of host publication | IFAC-PapersOnLine |
Subtitle of host publication | Proceedings 20th IFAC World Congress |
Editors | Denis Dochain, Didier Henrion, Dimitri Peaucelle |
Place of Publication | Laxenburg, Austria |
Publisher | Elsevier |
Pages | 10470-10475 |
Volume | 50-1 |
DOIs | |
Publication status | Published - 2017 |
Event | 20th World Congress of the International Federation of Automatic Control (IFAC), 2017 - Toulouse, France Duration: 9 Jul 2017 → 14 Jul 2017 Conference number: 20 https://www.ifac2017.org |
Publication series
Name | IFAC-PapersOnLine |
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Number | 1 |
Volume | 50 |
Conference
Conference | 20th World Congress of the International Federation of Automatic Control (IFAC), 2017 |
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Abbreviated title | IFAC 2017 |
Country/Territory | France |
City | Toulouse |
Period | 9/07/17 → 14/07/17 |
Internet address |
Keywords
- convex clustering
- Entropy maximization
- importance sampling
- linear programming