View source for Convex Optimal Uncertainty Quantification

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