Stochastic iterative algorithms have gained recent interest in machine learning and signal
processing for solving large-scale systems of equations, Ax=b. One such example is the …

The stochastic heavy ball momentum (SHBM) method has gained considerable popularity
as a scalable approach for solving large-scale optimization problems. However, one …

Sketch-and-project is a framework which unifies many known iterative methods for solving
linear systems and their variants, as well as further extensions to nonlinear optimization …

For solving a consistent system of linear equations, the Kaczmarz method is a popular
representative among iterative algorithms due to its simplicity and efficiency. Based on the …

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 …

The Kaczmarz method is a classical while effective iteration method for solving very large-
scale consistent systems of linear equations, and the randomized Kaczmarz method is an …

The Douglas–Rachford (DR) method is a widely used method for finding a point in the
intersection of two closed convex sets (feasibility problem). However, the method converges …

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 …

The Kaczmarz method for solving a linear system $ Ax= b $ interprets such a system as a
collection of equations $\left\langle a_i, x\right\rangle= b_i $, where $ a_i $ is the $ i $-th row …

The greedy randomized coordinate descent (GRCD) method is an effective iterative method
for solving large linear least-squares problems. In this work, we construct a class of relaxed …