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Applied and Computational Harmonic Analysis
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Applied and Computational Harmonic Analysis
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An iterative thresholding algorithm for linear inverse problems with multi-constraints and its applications

Authors: Khoramian, Saman;

An iterative thresholding algorithm for linear inverse problems with multi-constraints and its applications

Abstract

The minimization of functionals of the form \[ \Phi(f) = \| Kf-g \| ^2 + |\!|\!|f|\!|\!|_{W_1,p_1}^{p_1} + |\!|\!|f|\!|\!|_{W_2,p_2}^{p_2} + \cdots + |\!|\!|f|\!|\!|_{W_n,p_n}^{p_n}, \quad f \in \mathcal{H}, \] is considered, where \( K: \mathcal{H} \to \mathcal{H}^\prime \) is a bounded linear operator between Hilbert spaces \( \mathcal{H} \) and \( \mathcal{H}^\prime \), and \( g \in \mathcal{H}^\prime \). In addition we have \( 1 \leq p_i \leq 2 \) for \( i = 1,2,\dots, n \), and the notation \( |\!|\!|f|\!|\!|_{W_i,p_i} = (\sum_{\gamma\in \Gamma} w_{i,\gamma} | \langle f, \varphi_\gamma \rangle |^{p_i})^{1/p_i} \) for \( i = 1,2,\dots, n \) is used, where \( \{\varphi_\gamma\}_{\gamma \in \Gamma} \) denotes an orthonormal basis of the Hilbert space \( \mathcal{H} \), and \( W_i = \{w_{i,\gamma}\}_{\gamma \in \Gamma} \) denotes a system of nonnegative weights (\(i = 1,2,\dots, n\)). Under the assumption \( \| K \| < 1 \) and some other conditions, the author considers an iterative process of the form \( f^m = S(f^{m-1}+K^*(g-Kf^{m-1})), \;m = 1,2,\dots \), where \( f^0 \in \mathcal{H} \) is arbitrarily chosen, and \( K^* \) denotes the adjoint operator of \( K \), and \( S: \mathcal{H} \to \mathcal{H} \) is a nonexpansive operator specified in the paper. It is shown that this iterative process converges strongly to a minimizer of the considered functional \( \Phi \). In addition, a multiparameter Tikhonov type regularization of the form \[ \Phi_{\alpha,g}(f) = \| Kf-g \| ^2 + \alpha_1 |\!|\!|f|\!|\!|_{W_1,p_1}^{p_1} + \alpha_2 |\!|\!|f|\!|\!|_{W_2,p_2}^{p_2} + \cdots + \alpha_n |\!|\!|f|\!|\!|_{W_n,p_n}^{p_n}, \quad f \in \mathcal{H}, \] is considered, where \( \alpha_1, \dots, \alpha_n \) are nonnegative regularization parameters, and the notation \( \alpha = (\alpha_1, \dots, \alpha_n) \) is used here. It is shown that under the conditions \( \lim_{\epsilon \to 0} \alpha_i(\epsilon) = 0, \;\lim_{\epsilon \to 0} \frac{\epsilon^2}{\alpha_i(\epsilon)} = 0 \) and \( \lim_{\epsilon \to 0} \frac{\alpha_i(\epsilon)}{\alpha_j(\epsilon)} = 1 \) for each \( 1 \leq i, j \leq n \) and some other conditions specified in the paper, a regularizing scheme is obtained, i.e., for each \( f_0 \in \mathcal{H} \) one has \[ \lim_{\epsilon \to 0} \sup_{\| g - Kf_0\| < \epsilon} \| f_{\alpha(\epsilon),g}^*-f^\dagger \| = 0 . \] Here \( f^\dagger \) denotes the unique minimizer of the functional \(|\!|\!|\cdot|\!|\!|_{W_1,p_1}^{p_1} + \cdots + |\!|\!|\cdot|\!|\!|_{W_n,p_n}^{p_n} \) on \( N(K)+f_0 = \{f \in \mathcal{H} : Kf = Kf_0 \} \), and \( f_{\alpha,g}^* \) denotes the minimizer of the functional \( \Phi_{\alpha,g} \). Finally some applications are presented.

Related Organizations
Keywords

Linear inverse problem, Numerical optimization and variational techniques, Applied Mathematics, minimization of functionals, Hilbert space, linear inverse problem, Numerical solution to inverse problems in abstract spaces, multi-constraints, General harmonic expansions, frames, Tikhonov-type regularization, Multi-constraints, regularization, Multi-frame, Numerical solutions to equations with linear operators, Regularization, Other numerical methods in calculus of variations, frames, a priori parameter choice, Minimizer, linear ill-posed problem

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
18
Top 10%
Top 10%
Average
hybrid