The right–left preconditioning technique for the solution of the large matrix equationAXB = C
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Publication:5739609
DOI10.1080/00207160.2015.1045420zbMath1343.65041OpenAlexW2080058947MaRDI QIDQ5739609
Publication date: 19 July 2016
Published in: International Journal of Computer Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207160.2015.1045420
numerical experimentsiterative methodorthogonalizationpseudo-inversepreconditionergeneralized inverseslinear matrix equationsymmetric positive matrixglobal least squares algorithm
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Related Items (3)
Global-DGMRES method for matrix equationAXB = C ⋮ Matrix LSQR algorithm for structured solutions to quaternionic least squares problem ⋮ A gradient based iterative method and associated preconditioning technique for solving the large multilinear systems
Cites Work
- Nested splitting conjugate gradient method for matrix equation \(AXB=C\) and preconditioning
- An extrapolated TSVD method for linear discrete ill-posed problems with Kronecker structure
- Gradient based iterative solutions for general linear matrix equations
- The block preconditioned \textit{LSQR} and \textit{GL}-\textit{LSQR} algorithms for the block partitioned matrices
- Iterative solutions of the generalized Sylvester matrix equations by using the hierarchical identification principle
- New approaches for solving large Sylvester equations
- CG-type algorithms to solve symmetric matrix equations
- Global least squares method (Gl-LSQR) for solving general linear systems with several right-hand sides
- Iterative orthogonal direction methods for Hermitian minimum norm solutions of two consistent matrix equations
- LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
- Application of ADI Iterative Methods to the Restoration of Noisy Images
- Best Approximate Solution of Matrix Equation AXB+CYD=E
- Block Krylov subspace methods for solving large Sylvester equations
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