On the Kaczmarz methods based on relaxed greedy selection for solving matrix equation \(A X B = C\)

DOI10.1016/j.cam.2022.114374zbMath1505.65179OpenAlexW4224271242WikidataQ113878720 ScholiaQ113878720MaRDI QIDQ2146340

Publication date: 16 June 2022

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2022.114374

zbMATH Keywords

convergence analysis linear matrix equation Kaczmarz iterate Petrov-Galerkin conditions relaxed greedy selection

Mathematics Subject Classification ID

Matrix equations and identities (15A24) Numerical methods for matrix equations (65F45)

Related Items (1)

On the parameterized two-step iteration method for solving the matrix equation \(AXB=C\)

Cites Work

Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
On Hermitian and skew-Hermitian splitting iteration methods for the linear matrix equation \(AXB=C\)
Weighted progressive iteration approximation and convergence analysis
Acceleration of randomized Kaczmarz method via the Johnson-Lindenstrauss lemma
On the symmetric solutions of linear matrix equations
A randomized Kaczmarz algorithm with exponential convergence
On convergence rate of the randomized Kaczmarz method
On relaxed greedy randomized Kaczmarz methods for solving large sparse linear systems
The Jacobi and Gauss-Seidel-type iteration methods for the matrix equation \(A X B = C\)
On Motzkin's method for inconsistent linear systems
Stationary splitting iterative methods for the matrix equation \(AXB=C\)
A doubly stochastic block Gauss-Seidel algorithm for solving linear equations
On partially randomized extended Kaczmarz method for solving large sparse overdetermined inconsistent linear systems
A linearly convergent doubly stochastic Gauss-Seidel algorithm for solving linear equations and a certain class of over-parameterized optimization problems
Iterative solutions of the generalized Sylvester matrix equations by using the hierarchical identification principle
An iterative method for the least squares symmetric solution of the linear matrix equation \(AXB = C\)
Fundamentals of Computerized Tomography
Randomized Extended Kaczmarz for Solving Least Squares
On Hermitian and Skew-Hermitian Splitting Ietration Methods for the Continuous Sylvester Equations
Computational Methods for Linear Matrix Equations
Convergence Properties of the Randomized Extended Gauss--Seidel and Kaczmarz Methods
Iterative orthogonal direction methods for Hermitian minimum norm solutions of two consistent matrix equations
Kronecker Products, Unitary Matrices and Signal Processing Applications
Hermitian and Skew-Hermitian Splitting Methods for Non-Hermitian Positive Definite Linear Systems
On Greedy Randomized Kaczmarz Method for Solving Large Sparse Linear Systems
On Adaptive Sketch-and-Project for Solving Linear Systems
Greed Works: An Improved Analysis of Sampling Kaczmarz--Motzkin
A New Theoretical Estimate for the Convergence Rate of the Maximal Weighted Residual Kaczmarz Algorithm
Tight upper bounds for the convergence of the randomized extended Kaczmarz and Gauss–Seidel algorithms
A Sampling Kaczmarz--Motzkin Algorithm for Linear Feasibility
Rows versus Columns: Randomized Kaczmarz or Gauss--Seidel for Ridge Regression
Best Approximate Solution of Matrix Equation AXB+CYD=E
Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm

This page was built for publication: On the Kaczmarz methods based on relaxed greedy selection for solving matrix equation \(A X B = C\)