5 citations · 6 across the 10 of their papers we have counts for
10 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…
Acceleration and restart for the randomized Bregman-Kaczmarz method
Lionel Tondji, Ion Necoara, Dirk A. Lorenz
Optimizing strongly convex functions subject to linear constraints is a fundamental problem with numerous applications. In this work, we propose a block (accelerated) randomized Br…
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…
Linearly convergent adjoint free solution of least squares problems by random descent
Dirk A. Lorenz, Felix Schneppe, Lionel Tondji
We consider the problem of solving linear least squares problems in a framework where only evaluations of the linear map are possible. We derive randomized methods that do not need…
Faster Randomized Block Sparse Kaczmarz by Averaging
Lionel Tondji, Dirk A Lorenz
The standard randomized sparse Kaczmarz (RSK) method is an algorithm to compute sparse solutions of linear systems of equations and uses sequential updates, and thus, does not take…
Extended Randomized Kaczmarz Method for Sparse Least Squares and Impulsive Noise Problems
Frank Schöpfer, Dirk A Lorenz, Lionel Tondji +1
The Extended Randomized Kaczmarz method is a well known iterative scheme which can find the Moore-Penrose inverse solution of a possibly inconsistent linear system and requires onl…
Variance Reduction in Deep Learning: More Momentum is All You Need
Lionel Tondji, Sergii Kashubin, Moustapha Cisse
Variance reduction (VR) techniques have contributed significantly to accelerating learning with massive datasets in the smooth and strongly convex setting (Schmidt et al., 2017; Jo…