In this paper, we present convergence guarantees for a modified trust-region method designed for minimizing objective functions whose value is computed with noise and for which gradient and Hessian estimates are inexact and possibly random. In order to account for the noise, the method utilizes a relaxed step acceptance criterion and a cautious trust-region radius updating strategy. Under different inexact probabilistic zeroth-, first-, and second-order oracles we derive high probability tail bounds on the iteration complexity for convergence to points that satisfy approximate first- and second-order optimality conditions. Finally, we present two sets of numerical results. We first explore the tightness of our theoretical results on an example with adversarial noise in the function evaluations. We then investigate the performance of the modified trust-region algorithm on standard derivative-free optimization problems.
Citation
May 2022