Hanu, Matei and Latz, J. and Schillings, Claudia
(2023)
*Subsampling in ensemble Kalman inversion.*
ArXiv
.
(Submitted)

Full text not available from this repository.

Official URL: https://doi.org/10.48550/arXiv.2302.11323

## Abstract

We consider the Ensemble Kalman Inversion which has been recently introduced as an efficient, gradient-free optimisation method to estimate unknown parameters in an inverse setting. In the case of large data sets, the Ensemble Kalman Inversion becomes computationally infeasible as the data misfit needs to be evaluated for each particle in each iteration. Here, randomised algorithms like stochastic gradient descent have been demonstrated to successfully overcome this issue by using only a random subset of the data in each iteration, so-called subsampling techniques. Based on a recent analysis of a continuous-time representation of stochastic gradient methods, we propose, analyse, and apply subsampling-techniques within Ensemble Kalman Inversion. Indeed, we propose two different subsampling techniques: either every particle observes the same data subset (single subsampling) or every particle observes a different data subset (batch subsampling).

Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences > Mathematics > Applied Mathematics |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics > Deterministic and Stochastic PDEs Group |

ID Code: | 2980 |

Deposited By: | Ulrike Eickers |

Deposited On: | 22 May 2023 11:54 |

Last Modified: | 22 May 2023 11:56 |

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