Abstract
The least squares problems have wide applications in inverse Sturm–Liouville problem, particle physics and geology, inverse problems of vibration theory, control theory, digital image and signal processing. In this paper, we discuss the solution of the operator least squares problem. By extending the conjugate gradient least squares method, we propose an efficient matrix algorithm for solving the operator least squares problem. The matrix algorithm can find the solution of the problem within a finite number of iterations in the absence of round-off errors. Some numerical examples are given to illustrate the effectiveness of the matrix algorithm.
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Communicated by Ilio Galligani.
Appendix
Appendix
1.1 The proof of Lemma 3.1
Step 1. Noting that the inner product is commutative, it is enough to prove three statements of Lemma 3.1 for \(1\le u< v\le r.\) For \(u=1\) and \(v=2\), we get
and
Hence, (I)–(III) hold for \(u=1\) and \(v=2\).
Step 2. In this step, for \(u<w<r\), we assume that
We can get
and
Furthermore, for \(u=w\), we can write
and
By considering Steps 1 and 2, three statements of Lemma 3.1 hold by the principle of induction.
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Hajarian, M. Least Squares Solution of the Linear Operator Equation. J Optim Theory Appl 170, 205–219 (2016). https://doi.org/10.1007/s10957-015-0737-5
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DOI: https://doi.org/10.1007/s10957-015-0737-5