Two Relaxed Gradient-Based Algorithms for the Hermitian and Skew-Hermitian Solutions of the Linear Matrix Equation AXB + CXD = F

2019 ◽  
Vol 43 (5) ◽  
pp. 2343-2350
Author(s):  
Ahmed M. E. Bayoumi
Keyword(s):  
Matrix Equation ◽  
Linear Matrix ◽  
Linear Matrix Equation ◽  
Gradient Based

scholarly journals A Note on the⊤-Stein Matrix Equation

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Chun-Yueh Chiang
Keyword(s):  
Matrix Equation ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Comprehensive Treatment ◽  
Backward Error ◽  
Linear Matrix Equation ◽  
Perturbation Bounds ◽  
Necessary And Sufficient ◽  
The Relationship ◽  
Linear Matrix Equations

This note is concerned with the linear matrix equationX=AX⊤B + C, where the operator(·)⊤denotes the transpose (⊤) of a matrix. The first part of this paper sets forth the necessary and sufficient conditions for the unique solvability of the solutionX. The second part of this paper aims to provide a comprehensive treatment of the relationship between the theory of the generalized eigenvalue problem and the theory of the linear matrix equation. The final part of this paper starts with a brief review of numerical methods for solving the linear matrix equation. In relation to the computed methods, knowledge of the residual is discussed. An expression related to the backward error of an approximate solution is obtained; it shows that a small backward error implies a small residual. Just like the discussion of linear matrix equations, perturbation bounds for solving the linear matrix equation are also proposed in this work.Erratum to “A Note on the⊤-Stein Matrix Equation”

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