Matrix iterative solutions to the least squares problem of \(BXA^{T} = F\) with some linear constraints

DOI10.1016/j.amc.2006.06.097zbMath1117.65056OpenAlexW2035625942MaRDI QIDQ870162

Yuyang Qiu, Zhenyue Zhang, Jun-Feng Lu

Publication date: 12 March 2007

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

Full work available at URL: https://doi.org/10.1016/j.amc.2006.06.097

zbMATH Keywords

iterative algorithmnumerical examplesmatrix equationLSQR algorithm

Mathematics Subject Classification ID

Related Items (9)

Solving linearly constrained matrix least squares problem by LSQR ⋮ An iteration method to solve multiple constrained least squares problems ⋮ Least squares solution with the minimum-norm to general matrix equations via iteration ⋮ An iterative method for the least squares solutions of the linear matrix equations with some constraint ⋮ Algorithm for inequality-constrained least squares problems ⋮ A hybrid algorithm for solving minimization problem over (R,S)-symmetric matrices with the matrix inequality constraint ⋮ New symmetry preserving method for optimal correction of damping and stiffness matrices using measured modes ⋮ Iterative methods for solving consistent or inconsistent matrix inequality \({AXB} \geqslant C\) with linear constraints ⋮ Weighted least squares solutions to general coupled Sylvester matrix equations

Uses Software

• LSQR
• CRAIG

Cites Work

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• The symmetric Procrustes problem
• Computing a nearest symmetric positive semidefinite matrix
• On solutions of matrix equation \(AXB+CYD=F\)
• Perturbation analysis of the canonical correlations of matrix pairs
• Hermitian, Hermitian \(R\)-symmetric, and Hermitian \(R\)-skew symmetric Procrustes problems
• An iterative method for the least squares symmetric solution of the linear matrix equation \(AXB = C\)
• A Constrained Procrustes Problem
• Computing the Generalized Singular Value Decomposition
• LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
• Large Least Squares Problems Involving Kronecker Products
• Least Squares Solution of Matrix Equation $AXB^* + CYD^* = E$
• Least Squares Solution of BXA^T=T over Symmetric, Skew-Symmetric, and Positive Semidefinite X
• Calculating the Singular Values and Pseudo-Inverse of a Matrix
• Methods of conjugate gradients for solving linear systems

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