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

Understand

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.