# Iterative ensemble smoother as an approximate solution to a regularized minimum-average-cost problem: theory and applications

arXiv: https://arxiv.org/abs/1505.01135 · HTML: https://mlsanity.com/paper/1505.01135/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2015.

## Built on by (most cited first, 1 of 1)

- [Mitigating loss of variance in ensemble data assimilation: machine learning-based and distance-free localization](https://mlsanity.com/paper/2506.13362/) (2025)

## Abstract (arXiv)

The focus of this work is on an alternative implementation of the iterative ensemble smoother (iES). We show that iteration formulae similar to those used in citechen2013-levenberg,emerick2012ensemble can be derived by adopting a regularized Levenberg-Marquardt (RLM) algorithm citejin2010regularized to approximately solve a minimum-average-cost (MAC) problem. This not only leads to an alternative theoretical tool in understanding and analyzing the behaviour of the aforementioned iES, but also provides insights and guidelines for further developments of the smoothing algorithms. For illustration, we compare the performance of an implementation of the RLM-MAC algorithm to that of the approximate iES used in citechen2013-levenberg in three numerical examples: an initial condition estimation problem in a strongly nonlinear system, a facies estimation problem in a 2D reservoir and the history matching problem in the Brugge field case. In these three specific cases, the RLM-MAC algorithm exhibits comparable or even better performance, especially in the strongly nonlinear system.