@inproceedings{
wang2023direct,
title={{DIRECT} Optimisation with Bayesian Insights: Assessing Reliability Under Fixed Computational Budgets},
author={Fu Wang and Zeyu Fu and Xiaowei Huang and Wenjie Ruan},
booktitle={OPT 2023: Optimization for Machine Learning},
year={2023},
url={https://openreview.net/forum?id=9LSbN0sHwv}
}
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.
DIRECT Optimisation with Bayesian Insights: Assessing Reliability Under Fixed Computational Budgets
Fu Wang Zeyu Fu Xiaowei Huang Wenjie Ruan