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Distributionally Robust Chance Constrained Geometric Optimization

Banff International Research Station for Mathematical Innovation and Discovery

In this talk, we discuss distributionally robust geometric programs with individual and joint chance constraints. We consider three groups of uncertainty sets, namely uncertainty sets with known two first order moments information, uncertainty sets considering the uncertainties in terms of the distribution and the moments, and uncertainty sets constrained by the Kullback-Leibler divergence distance with a normal reference distribution. We present tractable reformulations for geometric programs with individual chance constraints for the three uncertainty sets. Efficient approximations are given for distributionally robust programs with joint chance constraints using piecewise linear functions. Numerical results are given on a shape optimization problem.

Mathematics; Operations research, mathematical programming; Probability theory and stochastic processes; Operation research

video/mp4

Author affiliation: Laboratoire de Recherche en Informatique - University Paris Sud

2018-09-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/67104

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/