Convergence of proximal algorithms with stepsize controls for non-linear inverse problems and application to sparse non-negative matrix factorization

We consider a general ill-posed inverse problem in a Hilbert space setting by minimizing a misfit functional coupling with a multi-penalty regularization for stabilization. For solving this minimization problem, we investigate two proximal algorithms with stepsize controls: a proximal fixed point al...

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Vydané v:	Numerical algorithms Ročník 85; číslo 4; s. 1255 - 1279
Hlavní autori:	Pham, Quy Muoi, Lachmund, Delf, Hào, Dinh Nho
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.12.2020 Springer Nature B.V
Predmet:	Algebra Algorithms Computer Science Convergence Exact solutions Factorization Hilbert space Inverse problems Mathematical analysis Nonlinear control Numeric Computing Numerical Analysis Operators (mathematics) Original Paper Regularization Regularization methods Sparse matrices Sparsity Theory of Computation Non-smooth Optimization Proximal fixed point iteration Alternating proximal iteration Sparse non-negative matrix factorization Non-linear inverse problems Sparsity regularization Sparse matrix factorization 65Kxx Non-negative sparsity regularization 49Mxx
ISSN:	1017-1398, 1572-9265
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