Iterative ensemble Kalman methods: A unified perspective with some new variants

Neil Kumar Chada, Yuming Chen, Daniel Sanz-Alonso

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


This paper provides a unified perspective of iterative ensemble Kalman methods, a family of derivative-free algorithms for parameter reconstruction and other related tasks. We identify, compare and develop three subfamilies of ensemble methods that differ in the objective they seek to minimize and the derivative-based optimization scheme they approximate through the ensemble. Our work emphasizes two principles for the derivation and analysis of iterative ensemble Kalman methods: statistical linearization and continuum limits. Following these guiding principles, we introduce new iterative ensemble Kalman methods that show promising numerical performance in Bayesian inverse problems, data assimilation and machine learning tasks.
Original languageEnglish (US)
JournalFoundations of Data Science
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-05-05
Acknowledgements: This work was funded in part by King Abdullah University of Science and Technology (KAUST). Hayssam Dahrouj would like to thank Effat University in Jeddah, Saudi Arabia, for funding the research reported in this paper through the Research and Consultancy Institute.


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