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<p>htdb2wiki: creating page for 2013p_han+13-siopt.html</p> <p><b>New page</b></p><div>{{HTDB paper<br /> | authors = Shuo Han, Molei Tao, Ufuk Topcu, Houman Owhadi, and Richard M. Murray<br /> | title = Convex Optimal Uncertainty Quantification<br /> | source = Submitted, SIAM Journal on Optimization (28 Nov 2013)<br /> | year = 2013<br /> | type = Journal submission<br /> | funding = NetSE<br /> | url = http://www.cds.caltech.edu/~murray/preprints/han+13-siopt_s.pdf<br /> | abstract = <br /> 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.<br /> | flags = <br /> | filetype = PDF<br /> | filesize = 549K<br /> | tag = han+13-siopt<br /> | id = 2013p<br /> }}</div>

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