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math.NA2025
The Stochastic Steepest Descent Method for Robust Optimization in Banach Spaces
Neil K. Chada, Philip J. Herbert
Stochastic gradient methods have been a popular and powerful choice of optimization methods, aimed at minimizing functions. Their advantage lies in the fact that that one approxima…
math.NA2024
The Ensemble Kalman Filter for Dynamic Inverse Problems
Simon Weissmann, Neil K. Chada, Xin T. Tong
In inverse problems, the goal is to estimate unknown model parameters from noisy observational data. Traditionally, inverse problems are solved under the assumption of a fixed forw…
math.NA2024
A Stochastic Iteratively Regularized Gauss-Newton Method
El Houcine Bergou, Neil K. Chada, Youssef Diouane
This work focuses on developing and motivating a stochastic version of a wellknown inverse problem methodology. Specifically, we consider the iteratively regularized Gauss-Newton m…