Randomized iterative algorithms have recently been proposed to solve large-scale linear
systems. In this paper, we present a simple randomized extended average block Kaczmarz …

Often in applications ranging from medical imaging and sensor networks to error correction
and data science (and beyond), one needs to solve large-scale linear systems in which a …

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 …

We consider linear systems where consists of normalized rows,, and where up to entries of
have been corrupted (possibly by arbitrarily large numbers). Haddock, Needell, Rebrova & …

Measurement data in linear systems arising from real-world applications often suffers from
both large, sparse corruptions, and widespread small-scale noise. This can render many …

When solving noisy linear systems Ax= b+ c, the theoretical and empirical performance of
stochastic iterative methods, such as the Randomized Kaczmarz algorithm, depends on the …

With the growth of large data as well as large-scale learning tasks, the need for efficient and
robust linear system solvers is greater than ever. The randomized Kaczmarz method (RK) …

For solving the large-scale linear systems, a unified randomized row iterative (RRI) method
was proposed in Gower and Richtárik (2015 SIAM J. Matrix Anal. Appl. 36 1660–1690) …

We study a version of the randomized Kaczmarz algorithm for solving systems of linear
equations where the iterates are confined to the solution space of a selected subsystem. We …

The randomized Kaczmarz method, along with its recently developed variants, has become
a popular tool for dealing with large-scale linear systems. However, these methods usually …