An analytical framework for consensus-based global optimization method
In this paper, we provide an analytical framework for investigating the efficiency of a consensus-based model for tackling global optimization problems. This work justifies the optimization algorithm in the mean-field sense showing the convergence to the global minimizer for a large class of functio...
| मुख्य लेखकों: | , , , |
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| स्वरूप: | Journal article |
| भाषा: | English |
| प्रकाशित: |
World Scientific Publishing
2018
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| _version_ | 1826262157967228928 |
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| author | Carrillo de la Plata, JA Choi, Y-P Totzeck, C Tse, O |
| author_facet | Carrillo de la Plata, JA Choi, Y-P Totzeck, C Tse, O |
| author_sort | Carrillo de la Plata, JA |
| collection | OXFORD |
| description | In this paper, we provide an analytical framework for investigating the efficiency of a consensus-based model for tackling global optimization problems. This work justifies the optimization algorithm in the mean-field sense showing the convergence to the global minimizer for a large class of functions. Theoretical results on consensus estimates are then illustrated by numerical simulations where variants of the method including nonlinear diffusion are introduced. |
| first_indexed | 2024-03-06T19:31:57Z |
| format | Journal article |
| id | oxford-uuid:1dc455e5-0b4c-4fff-985d-0b49d5d3839f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:31:57Z |
| publishDate | 2018 |
| publisher | World Scientific Publishing |
| record_format | dspace |
| spelling | oxford-uuid:1dc455e5-0b4c-4fff-985d-0b49d5d3839f2022-03-26T11:12:43ZAn analytical framework for consensus-based global optimization methodJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:1dc455e5-0b4c-4fff-985d-0b49d5d3839fEnglishSymplectic ElementsWorld Scientific Publishing2018Carrillo de la Plata, JAChoi, Y-PTotzeck, CTse, OIn this paper, we provide an analytical framework for investigating the efficiency of a consensus-based model for tackling global optimization problems. This work justifies the optimization algorithm in the mean-field sense showing the convergence to the global minimizer for a large class of functions. Theoretical results on consensus estimates are then illustrated by numerical simulations where variants of the method including nonlinear diffusion are introduced. |
| spellingShingle | Carrillo de la Plata, JA Choi, Y-P Totzeck, C Tse, O An analytical framework for consensus-based global optimization method |
| title | An analytical framework for consensus-based global optimization method |
| title_full | An analytical framework for consensus-based global optimization method |
| title_fullStr | An analytical framework for consensus-based global optimization method |
| title_full_unstemmed | An analytical framework for consensus-based global optimization method |
| title_short | An analytical framework for consensus-based global optimization method |
| title_sort | analytical framework for consensus based global optimization method |
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