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In this work we introduce the stochastic nonlinear constrainedderivative-free optimization method (S)NOWPAC (Stochastic NonlinearOptimization With Path-Augmented Constraints). The method extends thederivative-free optimizer NOWPAC to be applicable for optimization underuncertainty. It is based on a trust-region framework, utilizing local fullyquadratic surrogate models combined with Gaussian process surrogates tomitigate the noise in the objective function and constraint evaluations. Weshow the performance of our algorithm on a variety of robust optimizationproblems from the CUTEst benchmark suite by comparing to other state-of-the-artoptimization methods. Although we focus on robust optimization benchmarkproblems to demonstrate (S)NOWPAC's capabilities, the optimizer can be appliedto a broad range of applications in nonlinear constrained stochasticoptimization.

A trust-region method for derivative-free nonlinear constrained stochastic optimization