Convergence acceleration of ensemble Kalman inversion in nonlinear settings
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- by Neil K. Chada and Xin T. Tong;
- Math. Comp. 91 (2022), 1247-1280
- DOI: https://doi.org/10.1090/mcom/3709
- Published electronically: December 3, 2021
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Abstract:
Many data-science problems can be formulated as an inverse problem, where the parameters are estimated by minimizing a proper loss function. When complicated black-box models are involved, derivative-free optimization tools are often needed. The ensemble Kalman filter (EnKF) is a particle-based derivative-free Bayesian algorithm originally designed for data assimilation. Recently, it has been applied to inverse problems for computational efficiency. The resulting algorithm, known as ensemble Kalman inversion (EKI), involves running an ensemble of particles with EnKF update rules so they can converge to a minimizer. In this article, we investigate EKI convergence in general nonlinear settings. To improve convergence speed and stability, we consider applying EKI with non-constant step-sizes and covariance inflation. We prove that EKI can hit critical points with finite steps in non-convex settings. We further prove that EKI converges to the global minimizer polynomially fast if the loss function is strongly convex. We verify the analysis presented with numerical experiments on two inverse problems.References
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Bibliographic Information
- Neil K. Chada
- Affiliation: Applied Mathematics and Computational Science Program, King Abdullah University of Science and Technology, Thuwal 23955, Kingdom of Saudi Arabia
- MR Author ID: 1268846
- Email: neilchada123@gmail.com
- Xin T. Tong
- Affiliation: Department of Mathematics, National University of Singapore, 119077 Singapore
- ORCID: 0000-0002-8124-612X
- Email: mattxin@nus.edu.sg
- Received by editor(s): November 6, 2019
- Received by editor(s) in revised form: April 22, 2020, and August 17, 2021
- Published electronically: December 3, 2021
- Additional Notes: The first author was supported by a Singapore Ministry of Education Academic Research Funds Tier 2 grant [MOE2016-T2-2-135]. The research of the second author was supported by the Singapore Ministry of Education Academic Research Funds Tier 1 grant R-146-000-292-114
- © Copyright 2021 American Mathematical Society
- Journal: Math. Comp. 91 (2022), 1247-1280
- MSC (2020): Primary 49N45, 65K10, 90C56, 90C25
- DOI: https://doi.org/10.1090/mcom/3709
- MathSciNet review: 4405495