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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.  +
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
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15 May 2016 06:15:00  +
URL
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http://www.cds.caltech.edu/~murray/preprints/han+13-siopt_s.pdf  +
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