Efficient yield optimization with limited gradient information
Computational Engineering, Finance, and Science
2021-05-18 v1
Abstract
In this work an efficient strategy for yield optimization with uncertain and deterministic optimization variables is presented. The gradient based adaptive Newton-Monte Carlo method is modified, such that it can handle variables with (uncertain parameters) and without (deterministic parameters) analytical gradient information. This mixed strategy is numerically compared to derivative free approaches.
Cite
@article{arxiv.2105.07799,
title = {Efficient yield optimization with limited gradient information},
author = {Mona Fuhrländer and Sebastian Schöps},
journal= {arXiv preprint arXiv:2105.07799},
year = {2021}
}