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| Convex Optimal Uncertainty Quantification |
| Abstract |
Optimal uncertainty quantification (OUQ) i … Optimal uncertainty quantification (OUQ) is a framework for nu- merical extreme-case analysis of stochastic systems with imperfect knowl- edge of the underlying probability distribution and functions/events. This paper presents sufficient conditions (when underlying functions are known) under which an OUQ problem can be reformulated as a finite-dimensional convex optimization problem. e-dimensional convex optimization problem. +
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| Authors | Shuo Han, Molei Tao, Ufuk Topcu, Houman Owhadi, and Richard M. Murray + |
| ID | 2013p + |
| Source | Submitted, SIAM Journal on Optimization (28 Nov 2013) + |
| Tag | han+13-siopt + |
| Title | Convex Optimal Uncertainty Quantification + |
| Type | Journal submission + |
| Categories | Papers |
| Modification date This property is a special property in this wiki.
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15 May 2016 06:15:00 + |
| URL This property is a special property in this wiki.
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http://www.cds.caltech.edu/~murray/preprints/han+13-siopt_s.pdf + |
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| Convex Optimal Uncertainty Quantification + | Title |
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