Superiorization methodology and perturbation resilience of inertial proximal gradient algorithm with application to signal recovery
In this paper, we construct a novel algorithm for solving non-smooth composite optimization problems. By using inertial technique, we propose a modified proximal gradient algorithm with outer perturbations, and under standard mild conditions, we obtain strong convergence results for finding a soluti...
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| Veröffentlicht in: | The Journal of supercomputing 2020-12, Vol.76 (12), p.9456-9477 |
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| container_end_page | 9477 |
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| container_issue | 12 |
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| container_title | The Journal of supercomputing |
| container_volume | 76 |
| creator | Pakkaranang, Nuttapol Kumam, Poom Berinde, Vasile Suleiman, Yusuf I. |
| description | In this paper, we construct a novel algorithm for solving non-smooth composite optimization problems. By using inertial technique, we propose a modified proximal gradient algorithm with outer perturbations, and under standard mild conditions, we obtain strong convergence results for finding a solution of composite optimization problem. Based on bounded perturbation resilience, we present our proposed algorithm with the superiorization method and apply it to image recovery problem. Finally, we provide the numerical experiments to show efficiency of the proposed algorithm and comparison with previously known algorithms in signal recovery. |
| doi_str_mv | 10.1007/s11227-020-03215-z |
| format | Article |
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| subjects | Algorithms Compilers Computer Science High Performance Computing in Science and Engineering - CMMSE-2019 Interpreters Optimization Perturbation Processor Architectures Programming Languages Recovery Resilience Signal reconstruction |
| title | Superiorization methodology and perturbation resilience of inertial proximal gradient algorithm with application to signal recovery |
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