Convex Optimal Uncertainty Quantification

Shuo Han, Molei Tao, Ufuk Topcu, Houman Owhadi, and Richard M. Murray
Submitted, SIAM Journal on Optimization (28 Nov 2013)

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.

• Journal submission: http://www.cds.caltech.edu/~murray/preprints/han+13-siopt s.pdf
• Project(s): Template:HTDB funding::NetSE