A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909099185143808 |
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| author | Zheng, Zhong Ma, Shiqian Xue, Lingzhou |
| author_facet | Zheng, Zhong Ma, Shiqian Xue, Lingzhou |
| contents | This paper considers the robust phase retrieval problem, which can be cast as a nonsmooth and nonconvex optimization problem. We propose a new inexact proximal linear algorithm with the subproblem being solved inexactly. Our contributions are two adaptive stopping criteria for the subproblem. The convergence behavior of the proposed methods is analyzed. Through experiments on both synthetic and real datasets, we demonstrate that our methods are much more efficient than existing methods, such as the original proximal linear algorithm and the subgradient method. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2304_12522 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval Zheng, Zhong Ma, Shiqian Xue, Lingzhou Optimization and Control Machine Learning Signal Processing Computation This paper considers the robust phase retrieval problem, which can be cast as a nonsmooth and nonconvex optimization problem. We propose a new inexact proximal linear algorithm with the subproblem being solved inexactly. Our contributions are two adaptive stopping criteria for the subproblem. The convergence behavior of the proposed methods is analyzed. Through experiments on both synthetic and real datasets, we demonstrate that our methods are much more efficient than existing methods, such as the original proximal linear algorithm and the subgradient method. |
| title | A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval |
| topic | Optimization and Control Machine Learning Signal Processing Computation |
| url | https://arxiv.org/abs/2304.12522 |