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On the mean field limit of consensus based methods
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On the mean field limit of consensus based methods
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Consensus based optimization (CBO) employs a swarm of particles evolving as a system of stochastic differential equations (SDEs). Recently, it has been adapted to yield a derivative free sampling method referred to as consensus based sampling (CBS). In this paper, we investigate the ``mean field limit'' of a class of consensus methods, including CBO and CBS. This limit allows to characterize the system's behavior as the number of particles approaches infinity. Building upon prior work such as (Huang and Qiu, 2022), we establish the existence of a unique, strong solution for these finite-particle SDEs. We further provide uniform moment estimates, which allow to show a Fokker-Planck equation in the mean-field limit. Finally, we prove that the limiting McKean-Vlasov type SDE related to the Fokker-Planck equation admits a unique solution.
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Cited by 1 Pith paper
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Mean-Field Model for Two-Layer Neural Networks Trained with Consensus-Based Optimization
CBO can train small two-layer networks, a hybrid CBO-Adam method improves convergence and stability, and a Wasserstein mean-field model of CBO has monotonically decreasing variance.
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