DIRECT Optimisation with Bayesian Insights: Assessing Reliability Under Fixed Computational Budgets
Abstract
We introduce a method for probabilistically evaluating the reliability of Lipschitzian global optimisation under a constrained computational budget, a context frequently encountered in various applications. By interpreting the slope data gathered during the optimisation process as samples from the objective function's derivative, we utilise Bayesian posterior prediction to derive a confidence score for the optimisation outcomes. We validate our approach using numerical experiments on four multi-dimensional test functions, and the results highlight the practicality and efficacy of our approach.
Cite
Text
Wang et al. "DIRECT Optimisation with Bayesian Insights: Assessing Reliability Under Fixed Computational Budgets." NeurIPS 2023 Workshops: OPT, 2023.Markdown
[Wang et al. "DIRECT Optimisation with Bayesian Insights: Assessing Reliability Under Fixed Computational Budgets." NeurIPS 2023 Workshops: OPT, 2023.](https://mlanthology.org/neuripsw/2023/wang2023neuripsw-direct/)BibTeX
@inproceedings{wang2023neuripsw-direct,
title = {{DIRECT Optimisation with Bayesian Insights: Assessing Reliability Under Fixed Computational Budgets}},
author = {Wang, Fu and Fu, Zeyu and Huang, Xiaowei and Ruan, Wenjie},
booktitle = {NeurIPS 2023 Workshops: OPT},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/wang2023neuripsw-direct/}
}