A Stepwise Uncertainty Reduction Approach to Constrained Global Optimization
Abstract
Using statistical emulators to guide sequential evaluations of complex computer experiments is now a well-established practice. When a model provides multiple outputs, a typical objective is to optimize one of the outputs with constraints (for instance, a threshold not to exceed) on the values of the other outputs. We propose here a new optimization strategy based on the stepwise uncertainty reduction paradigm, which offers an efficient trade-off between exploration and local search near the boundaries. The strategy is illustrated on numerical examples.
Cite
Text
Picheny. "A Stepwise Uncertainty Reduction Approach to Constrained Global Optimization." International Conference on Artificial Intelligence and Statistics, 2014.Markdown
[Picheny. "A Stepwise Uncertainty Reduction Approach to Constrained Global Optimization." International Conference on Artificial Intelligence and Statistics, 2014.](https://mlanthology.org/aistats/2014/picheny2014aistats-stepwise/)BibTeX
@inproceedings{picheny2014aistats-stepwise,
title = {{A Stepwise Uncertainty Reduction Approach to Constrained Global Optimization}},
author = {Picheny, Victor},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2014},
pages = {787-795},
url = {https://mlanthology.org/aistats/2014/picheny2014aistats-stepwise/}
}