Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators
Abstract
Constrained optimization of the parameters of a simulator plays a crucial role in a design process. These problems become challenging when the simulator is stochastic, computationally expensive, and the parameter space is high-dimensional. One can efficiently perform optimization only by utilizing the gradient with respect to the parameters, but these gradients are unavailable in many legacy, black-box codes. We introduce the algorithm Scout-Nd (Stochastic Constrained Optimization for N dimensions) to tackle the issues mentioned earlier by efficiently estimating the gradient, reducing the noise of the gradient estimator, and applying multi-fidelity schemes to further reduce computational effort. We validate our approach on standard benchmarks, demonstrating its effectiveness in optimizing parameters highlighting better performance compared to existing methods.
Cite
@article{arxiv.2311.15137,
title = {Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators},
author = {Atul Agrawal and Kislaya Ravi and Phaedon-Stelios Koutsourelakis and Hans-Joachim Bungartz},
journal= {arXiv preprint arXiv:2311.15137},
year = {2023}
}