Abstract
This study presents some new iterative algorithms based on the gradient method to solve general constrained systems of conjugate transpose matrix equations for both real and complex matrices. In addition, we analyze the convergence properties of these methods and provide numerical techniques to determine the solutions. Then we prove that the optimal parameters of the new algorithm satisfy a constrained optimization problem. The effectiveness of the proposed iterative methods is demonstrated through various numerical examples employed in this study and compared the results by some existing algorithms.
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Appendix A
Appendix A
The proof of Theorem 3.8: Initially, we define the error matrix as follows:
Employing the procedure outlined in Algorithm 4 will yield the subsequent outcome
where
By considering the definition of the Frobenius norm, we obtain:
It is readily apparent that
and
Merging these relationships with equation (6.2) yields:
Now from the definition of Frobenius inner product and the fact that for two matrices \( L_1 \) and \( L_2 \), \( \left\langle L_1, L_2 \right\rangle _{F}+ \left\langle L_2, L_1 \right\rangle _{F}\) is real and \( -\left\langle L_1, L_2 \right\rangle _{F}- \left\langle L_2, L_1 \right\rangle _{F} \le \Vert L_{1}\Vert _F^2+\Vert L_2\Vert _F^2\) we obtain
Then by using Lemmas 2.2 and 2.3, one can ascertain that:
and
Given these inequalities and (6.7), it can be concluded that:
where \( \theta _{1} \) and \(\theta _{2} \) are defined in (3.39) and (3.40), respectively. Now there are \( \epsilon _{\tau }>0, \tau =1,..., \gamma , \) such that
where \( \epsilon =\max _{1\le \tau \le \gamma } \{\epsilon _{\tau }\} \). Hence we conclude
Now (6.11) yields:
If the parameters \( \omega _{1} \) and \( \omega _{2} \) are selected according to (3.38), then it follows that:
Consequences drawn from the convergence theorem of series indicate that:
Finally given that the matrix equation (3.31) has a unique solution, it can be concluded that \( \lim \limits _{\varsigma \longrightarrow \infty } L(\varsigma )=L^{\star }\).
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Shirilord, A., Dehghan, M. Solving a system of complex matrix equations using a gradient-based method and its application in image restoration. Numer Algor (2024). https://doi.org/10.1007/s11075-024-01856-2
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DOI: https://doi.org/10.1007/s11075-024-01856-2
Keywords
- Iterative methods
- Convergence
- Conjugate transpose matrix equation
- System theory
- Discrete-time antilinear system
- Signal processing