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

Abstract

In this work we introduce the stochastic nonlinear constrained derivative-free optimization method (S)NOWPAC (Stochastic Nonlinear Optimization With Path-Augmented Constraints). The method extends the derivative-free optimizer NOWPAC to be applicable for optimization under uncertainty. It is based on a trust-region framework, utilizing local fully quadratic surrogate models combined with Gaussian process surrogates to mitigate the noise in the objective function and constraint evaluations. We show the performance of our algorithm on a variety of robust optimization problems from the CUTEst benchmark suite by comparing to other state-of-the-art optimization methods. Although we focus on robust optimization benchmark problems to demonstrate (S)NOWPAC's capabilities, the optimizer can be applied to a broad range of applications in nonlinear constrained stochastic optimization.