Tikhonov regularized iterative methods for nonlinear problems
We consider the monotone inclusion problems in real Hilbert spaces. Proximal splitting algorithms are very popular technique to solve it and generally achieve weak convergence under mild assumptions. Researchers assume the strong conditions like strong convexity or strong monotonicity on the conside...
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| Published in: | Optimization Vol. 73; no. 13; pp. 3787 - 3818 |
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Taylor & Francis
03.12.2024
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| ISSN: | 0233-1934, 1029-4945 |
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| Abstract | We consider the monotone inclusion problems in real Hilbert spaces. Proximal splitting algorithms are very popular technique to solve it and generally achieve weak convergence under mild assumptions. Researchers assume the strong conditions like strong convexity or strong monotonicity on the considered operators to prove strong convergence of the algorithms. Mann iteration method and normal S-iteration method are popular methods to solve fixed point problems. We propose a new common fixed point algorithm based on normal S-iteration method using Tikhonov regularization to find common fixed point of non-expansive operators and prove strong convergence of the generated sequence to the set of common fixed points without assuming strong convexity and strong monotonicity. Based on proposed fixed point algorithm, we propose a forward-backward-type algorithm and a Douglas-Rachford algorithm in connection with Tikhonov regularization to find the solution of monotone inclusion problems. Further, we consider the complexly structured monotone inclusion problems which are very popular these days. We also propose a strongly convergent forward-backward-type primal-dual algorithm and a Douglas-Rachford-type primal-dual algorithm to solve the monotone inclusion problems. Finally, we conduct a numerical experiment to solve image deblurring problems. |
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| AbstractList | We consider the monotone inclusion problems in real Hilbert spaces. Proximal splitting algorithms are very popular technique to solve it and generally achieve weak convergence under mild assumptions. Researchers assume the strong conditions like strong convexity or strong monotonicity on the considered operators to prove strong convergence of the algorithms. Mann iteration method and normal S-iteration method are popular methods to solve fixed point problems. We propose a new common fixed point algorithm based on normal S-iteration method using Tikhonov regularization to find common fixed point of non-expansive operators and prove strong convergence of the generated sequence to the set of common fixed points without assuming strong convexity and strong monotonicity. Based on proposed fixed point algorithm, we propose a forward–backward-type algorithm and a Douglas–Rachford algorithm in connection with Tikhonov regularization to find the solution of monotone inclusion problems. Further, we consider the complexly structured monotone inclusion problems which are very popular these days. We also propose a strongly convergent forward–backward-type primal–dual algorithm and a Douglas–Rachford-type primal–dual algorithm to solve the monotone inclusion problems. Finally, we conduct a numerical experiment to solve image deblurring problems. |
| Author | Sahu, D. R. Gautam, Pankaj Dixit, Avinash Som, T. |
| Author_xml | – sequence: 1 givenname: Avinash surname: Dixit fullname: Dixit, Avinash organization: University of Delhi – sequence: 2 givenname: D. R. surname: Sahu fullname: Sahu, D. R. organization: Banaras Hindu University – sequence: 3 givenname: Pankaj orcidid: 0000-0001-7731-2529 surname: Gautam fullname: Gautam, Pankaj email: pgautam908@gmail.com organization: Indian Institute of Technology Madras – sequence: 4 givenname: T. surname: Som fullname: Som, T. organization: Indian Institute of Technology (BHU) |
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| SubjectTerms | Algorithms Convergence Convexity Douglas-Rachford algorithm Fixed points (mathematics) Fixed points of non-expansive mappings forward-backward algorithm Hilbert space Iterative methods Operators primal-dual algorithm Regularization Splitting splitting methods Tikhonov regularization |
| Title | Tikhonov regularized iterative methods for nonlinear problems |
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