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arXiv · 2404.10372

Consensus-based algorithms for stochastic optimization problems

Sabrina Bonandin·Michael Herty
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Abstract

We address an optimization problem where the cost function is the expectation of a random mapping. To tackle the problem two approaches based on the approximation of the objective function by consensus-based particle optimization methods on the search space are developed. The resulting methods are mathematically analyzed using a mean-field approximation and their connection is established. Several numerical experiments show the validity of the proposed algorithms and investigate their rates of convergence.

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Sabrina Bonandin, Michael Herty. 2024-04-16. Consensus-based algorithms for stochastic optimization problems. https://doi.org/10.1137/24m1654531

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