Data-driven Distributionally Robust Polynomial Optimization

Part of Advances in Neural Information Processing Systems 26 (NIPS 2013)

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Martin Mevissen, Emanuele Ragnoli, Jia Yuan Yu


We consider robust optimization for polynomial optimization problems where the uncertainty set is a set of candidate probability density functions. This set is a ball around a density function estimated from data samples, i.e., it is data-driven and random. Polynomial optimization problems are inherently hard due to nonconvex objectives and constraints. However, we show that by employing polynomial and histogram density estimates, we can introduce robustness with respect to distributional uncertainty sets without making the problem harder. We show that the solution to the distributionally robust problem is the limit of a sequence of tractable semidefinite programming relaxations. We also give finite-sample consistency guarantees for the data-driven uncertainty sets. Finally, we apply our model and solution method in a water network problem.