Expected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulators
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912216156995584 |
|---|---|
| author | Miller, John Joshua Mak, Simon Sun, Benny Narayanan, Sai Ranjeet Yang, Suo Sun, Zongxuan Kim, Kenneth S. Kweon, Chol-Bum Mike |
| author_facet | Miller, John Joshua Mak, Simon Sun, Benny Narayanan, Sai Ranjeet Yang, Suo Sun, Zongxuan Kim, Kenneth S. Kweon, Chol-Bum Mike |
| contents | The optimization of expensive black-box simulators arises in a myriad of modern scientific and engineering applications. Bayesian optimization provides an appealing solution, by leveraging a fitted surrogate model to guide the selection of subsequent simulator evaluations. In practice, however, the objective is often not to obtain a single good solution, but rather a ``basket'' of good solutions from which users can choose for downstream decision-making. This need arises in our motivating application for real-time control of internal combustion engines for flight propulsion, where a diverse set of control strategies is essential for stable flight control. There has been little work on this front for Bayesian optimization. We thus propose a new Expected Diverse Utility (EDU) method that searches for diverse ``$ε$-optimal'' solutions: locally-optimal solutions within a tolerance level $ε> 0$ from a global optimum. We show that EDU yields a closed-form acquisition function under a Gaussian process surrogate model, which facilitates efficient sequential queries via automatic differentiation. This closed form further reveals a novel exploration-exploitation-diversity trade-off, which incorporates the desired diversity property within the well-known exploration-exploitation trade-off. We demonstrate the improvement of EDU over existing methods in a suite of numerical experiments, then explore the EDU in two applications on rover trajectory optimization and engine control for flight propulsion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Expected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulators Miller, John Joshua Mak, Simon Sun, Benny Narayanan, Sai Ranjeet Yang, Suo Sun, Zongxuan Kim, Kenneth S. Kweon, Chol-Bum Mike Applications Machine Learning The optimization of expensive black-box simulators arises in a myriad of modern scientific and engineering applications. Bayesian optimization provides an appealing solution, by leveraging a fitted surrogate model to guide the selection of subsequent simulator evaluations. In practice, however, the objective is often not to obtain a single good solution, but rather a ``basket'' of good solutions from which users can choose for downstream decision-making. This need arises in our motivating application for real-time control of internal combustion engines for flight propulsion, where a diverse set of control strategies is essential for stable flight control. There has been little work on this front for Bayesian optimization. We thus propose a new Expected Diverse Utility (EDU) method that searches for diverse ``$ε$-optimal'' solutions: locally-optimal solutions within a tolerance level $ε> 0$ from a global optimum. We show that EDU yields a closed-form acquisition function under a Gaussian process surrogate model, which facilitates efficient sequential queries via automatic differentiation. This closed form further reveals a novel exploration-exploitation-diversity trade-off, which incorporates the desired diversity property within the well-known exploration-exploitation trade-off. We demonstrate the improvement of EDU over existing methods in a suite of numerical experiments, then explore the EDU in two applications on rover trajectory optimization and engine control for flight propulsion. |
| title | Expected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulators |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2410.01196 |