On a probabilistic global optimizer derived from the Walker slice sampling
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909379072098304 |
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| author | Gupta, Aditya Das, Souvik Chatterjee, Debasish |
| author_facet | Gupta, Aditya Das, Souvik Chatterjee, Debasish |
| contents | This article presents a zeroth order probabilistic global optimization algorithm -- SwiftNav -- for (not necessarily convex) functions over a compact domain. A discretization procedure is deployed on the compact domain, starting with a small step-size $h > 0$ and subsequently adaptively refining it in the course of a simulated annealing routine utilizing the Walker slice and the Gibbs sampler, in order to identify a set of global optimizers up to good precision. SwiftNav is parallelizable, which helps with scalability as the dimension of decision variables increases. Several numerical experiments are included here to demonstrate the effectiveness and accuracy of SwiftNav in high-dimensional benchmark optimization problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03851 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On a probabilistic global optimizer derived from the Walker slice sampling Gupta, Aditya Das, Souvik Chatterjee, Debasish Optimization and Control Mathematical Software This article presents a zeroth order probabilistic global optimization algorithm -- SwiftNav -- for (not necessarily convex) functions over a compact domain. A discretization procedure is deployed on the compact domain, starting with a small step-size $h > 0$ and subsequently adaptively refining it in the course of a simulated annealing routine utilizing the Walker slice and the Gibbs sampler, in order to identify a set of global optimizers up to good precision. SwiftNav is parallelizable, which helps with scalability as the dimension of decision variables increases. Several numerical experiments are included here to demonstrate the effectiveness and accuracy of SwiftNav in high-dimensional benchmark optimization problems. |
| title | On a probabilistic global optimizer derived from the Walker slice sampling |
| topic | Optimization and Control Mathematical Software |
| url | https://arxiv.org/abs/2411.03851 |