Ensemble-Based Gradient Inference for Particle Methods in Optimization and Sampling-Reference-Cited by-同舟云学术

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Ensemble-Based Gradient Inference for Particle Methods in Optimization and Sampling

Published:2023-07-10 Issue:3 Volume:11 Page:757-787
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

Schillings Claudia¹,Totzeck Claudia²^ORCID,Wacker Philipp³

Affiliation:

1. Institute of Mathematics, Freie Universität Berlin, Berlin 14195 Germany.

2. School of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal 42119 Germany.

3. School of Mathematics and Statistics, University of Canterbury, Christchurch 8140 New Zealand.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Discrete Mathematics and Combinatorics,Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference36 articles.

1. Evolutionary Algorithms in Theory and Practice

2. Well posedness and convergence analysis of the ensemble Kalman inversion

3. The Simplex Gradient and Noisy Optimization Problems

4. Consensus‐based sampling

5. An analytical framework for consensus-based global optimization method

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. EnKSGD: A Class of Preconditioned Black Box Optimization and Inversion Algorithms;SIAM Journal on Scientific Computing;2024-06-19

2. Polarized consensus-based dynamics for optimization and sampling;Mathematical Programming;2024-05-31

3. Consensus-based optimisation with truncated noise;European Journal of Applied Mathematics;2024-04-05

4. Leveraging memory effects and gradient information in consensus-based optimisation: On global convergence in mean-field law;European Journal of Applied Mathematics;2023-10-20

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