Details:
Speaker: An-Bao Xu
Title: Least-squares symmetric solution to the matrix equation A X B = C with the norm inequality constraint
In this talk, we are going to discuss an iterative method to compute symmetric least-squares solution of the matrix equation A X B = C with the norm inequality constraint,
\begin{equation*}
\mathop {\min }\limits_{X \in SR^{n\times n}} \textstyle{1 \over 2}\left\| {A\, X\, B\, - \,C} \right\|^2
\quad subject\ to \left\| X \right\| \le \Delta,
\end{equation*}
where \(A\in R^{p\times n}\), \(B\in R^{n\times q}\), \(C\in R^{p\times q}\), and \(\Delta\) is a nonnegative real number.
For this method, without the error of calculation, a desired solution can be obtained with finitely iterate steps. Numerical experiments are performed to illustrate the efficiency and application of the algorithm.
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