5 papers
A Tensor Greedy Double-Block Extended Kaczmarz Method for Inconsistent Tensor Linear Systems under the t-product
Jérémie Mabiala, Lionel Tondji
The randomized extended Kaczmarz method is an effective iterative framework for solving large-scale inconsistent linear systems. In this paper, we extend this framework to third-or…
Accelerated Exact Recovery from Noisy Data via Averaging and Noise-Aware Adaptive Bregman-Kaczmarz
Lionel Tondji, Abakar A. Mahamat, Idriss Tondji
The adaptive Bregman-Kaczmarz method recovers the exact, noise-free solution of a linear inverse problem even when every measurement it queries is corrupted, provided the corruptio…
Why the noise model matters: A performance gap in learned regularization
Sebastian Banert, Christoph Brauer, Dirk Lorenz +1
This article addresses the challenge of learning effective regularizers for linear inverse problems. We analyze and compare several types of learned variational regularization agai…
An accelerated randomized Bregman-Kaczmarz method for strongly convex linearly constraint optimization
Lionel Tondji, Dirk A. Lorenz, Ion Necoara
In this paper, we propose a randomized accelerated method for the minimization of a strongly convex function under linear constraints. The method is of Kaczmarz-type, i.e. it only…
Adaptive Bregman-Kaczmarz: An Approach to Solve Linear Inverse Problems with Independent Noise Exactly
Lionel Tondji, Idriss Tondji, Dirk A. Lorenz
We consider the block Bregman-Kaczmarz method for finite dimensional linear inverse problems. The block Bregman-Kaczmarz method uses blocks of the linear system and performs iterat…