Consider the following consistent Sylvester tensor equation \[\mathscr{X}\times_1 A +\mathscr{X}\times_2 B+\mathscr{X}\times_3 C=\mathscr{D},\] where the matrices $A,B, C$ and the tensor $\mathscr{D}$ are given and $\mathscr{X}$ is the unknown tensor. The current paper concerns with examining a simple and neat framework for accelerating the speed of convergence of the gradient-based iterative algorithm and its modified version for solving the mentioned Sylvester tensor equation without setting the restriction of the existence of a unique solution. Numerical experiments are reported which confirm the validity of the presented results.
Panjeh Ali Beik, F., & Ahmadi-Asl, S. (2015). Residual norm steepest descent based iterative algorithms for Sylvester tensor equations. Journal of Mathematical Modeling, 2(2), 115-131.
MLA
Fatemeh Panjeh Ali Beik; Salman Ahmadi-Asl. "Residual norm steepest descent based iterative algorithms for Sylvester tensor equations". Journal of Mathematical Modeling, 2, 2, 2015, 115-131.
HARVARD
Panjeh Ali Beik, F., Ahmadi-Asl, S. (2015). 'Residual norm steepest descent based iterative algorithms for Sylvester tensor equations', Journal of Mathematical Modeling, 2(2), pp. 115-131.
VANCOUVER
Panjeh Ali Beik, F., Ahmadi-Asl, S. Residual norm steepest descent based iterative algorithms for Sylvester tensor equations. Journal of Mathematical Modeling, 2015; 2(2): 115-131.
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