The Randomized Kaczmarz Method with Mismatched Adjoint

03/07/2018
by   Dirk A. Lorenz, et al.
0

This paper investigates the randomized version of the Kaczmarz method to solve linear systems in the case where the adjoint of the system matrix is not exact---a situation we refer to as "mismatched adjoint". We show that the method may still converge both in the over- and underdetermined consistent case under appropriate conditions, and we calculate the expected asymptotic rate of linear convergence. Moreover, we analyze the inconsistent case and obtain results for the method with mismatched adjoint as for the standard method. Finally, we derive a method to compute optimized probabilities for the choice of the rows and illustrate our findings with numerical example.

READ FULL TEXT
research
09/08/2022

On the Convergence of Randomized and Greedy Relaxation Schemes for Solving Nonsingular Linear Systems of Equations

We extend results known for the randomized Gauss-Seidel and the Gauss-So...
research
06/01/2020

Randomized Kaczmarz for Tensor Linear Systems

Solving linear systems of equations is a fundamental problem in mathemat...
research
01/21/2022

Extended Randomized Kaczmarz Method for Sparse Least Squares and Impulsive Noise Problems

The Extended Randomized Kaczmarz method is a well known iterative scheme...
research
03/21/2022

Faster Randomized Block Sparse Kaczmarz by Averaging

The standard randomized sparse Kaczmarz (RSK) method is an algorithm to ...
research
04/12/2021

Influences of Numerical Discretizations on Hitting Probabilities for Linear Stochastic Parabolic System

This paper investigates the influences of standard numerical discretizat...
research
02/10/2020

Randomized Kaczmarz with Averaging

The randomized Kaczmarz (RK) method is an iterative method for approxima...

Please sign up or login with your details

Forgot password? Click here to reset