Iterative algorithms for solving the matrix equation AXB+CXTD = E

Minghui Wang

doi:10.1016/j.amc.2006.08.169

Iterative algorithms for solving the matrix equation AXB+CXTD = E

2007-04-01

75

PRE

AI

DOI：10.1016/j.amc.2006.08.169

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In this paper, we propose two iterative algorithms to solve the matrix equation AXB + (CXD)-D-T = E. The first algorithm is applied when the matrix equation is consistent. In this case, for any (special) initial matrix X-1, a solution (the minimal Frobenius norm solution) can be obtained within finite iteration steps in the absence of roundoff errors. The second algorithm is applied when the matrix equation is inconsistent. In this case, for any (special) initial matrix X-1, a least squares solution (the minimal Frobenius norm least squares solution) can be obtained within finite iteration steps in the absence of roundoff errors. Some examples verify the efficiency of these algorithms. (c) 2006 Elsevier Inc. All rights reserved.

Keyword:

iterative algorithm

Kronecker product

conjugate gradient method

matrix equation

Iterative algorithms for solving the matrix equation AXB+CXTD = E

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