Dante Kalise
Consensus-based optimization via jump-diffusion stochastic differential equations
Kalise, Dante; Sharma, Akash; Tretyakov, Michael V.
Authors
Akash Sharma
Professor MIKHAIL TRETYAKOV Michael.Tretyakov@nottingham.ac.uk
PROFESSOR OF MATHEMATICS
Abstract
We introduce a new consensus-based optimization (CBO) method where an interacting particle system is driven by jump-diffusion stochastic differential equations (SDEs). We study well-posedness of the particle system as well as of its mean-field limit. The major contributions of this paper are proofs of convergence of the interacting particle system towards the mean-field limit and convergence of a discretized particle system towards the continuous-time dynamics in the mean-square sense. We also prove convergence of the mean-field jump-diffusion SDEs towards global minimizer for a large class of objective functions. We demonstrate improved performance of the proposed CBO method over earlier CBO methods in numerical simulations on benchmark objective functions.
Citation
Kalise, D., Sharma, A., & Tretyakov, M. V. (2023). Consensus-based optimization via jump-diffusion stochastic differential equations. Mathematical Models and Methods in Applied Sciences, 33(02), 289-339. https://doi.org/10.1142/S0218202523500082
Journal Article Type | Article |
---|---|
Acceptance Date | Dec 14, 2022 |
Online Publication Date | Feb 17, 2023 |
Publication Date | 2023-02 |
Deposit Date | Dec 19, 2022 |
Publicly Available Date | Jul 28, 2023 |
Journal | Mathematical Models and Methods in Applied Sciences |
Electronic ISSN | 0218-2025 |
Publisher | World Scientific |
Peer Reviewed | Peer Reviewed |
Volume | 33 |
Issue | 02 |
Pages | 289-339 |
DOI | https://doi.org/10.1142/S0218202523500082 |
Keywords | global non-convex optimization, interacting particle systems, mean-field jump-diffusion SDEs, McKean-Vlasov SDEs with jumps |
Public URL | https://nottingham-repository.worktribe.com/output/15159161 |
Publisher URL | https://www.worldscientific.com/doi/10.1142/S0218202523500082 |
Additional Information | Electronic version of an article published as Mathematical Models and Methods in Applied Sciences, 33, 02, 2023, 289-339 10.1142/s0218202523500082 |
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
© The Author(s)
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