A Family of Multi-Parameterized Proximal Point Algorithms
In this paper, a multi-parameterized proximal point algorithm combining with a relaxation step is developed for solving convex minimization problem subject to linear constraints. We show its global convergence and sublinear convergence rate from the prospective of variational inequality. Preliminary...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8894083/ |
| _version_ | 1811212401570217984 |
|---|---|
| author | Jianchao Bai Ke Guo Xiaokai Chang |
| author_facet | Jianchao Bai Ke Guo Xiaokai Chang |
| author_sort | Jianchao Bai |
| collection | DOAJ |
| description | In this paper, a multi-parameterized proximal point algorithm combining with a relaxation step is developed for solving convex minimization problem subject to linear constraints. We show its global convergence and sublinear convergence rate from the prospective of variational inequality. Preliminary numerical experiments on testing a sparse minimization problem from signal processing indicate that the proposed algorithm performs better than some well-established methods. |
| first_indexed | 2024-04-12T05:28:45Z |
| format | Article |
| id | doaj.art-3c55d9f81bf04c52b9fea7d9bdf2ac65 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T05:28:45Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3c55d9f81bf04c52b9fea7d9bdf2ac652022-12-22T03:46:10ZengIEEEIEEE Access2169-35362019-01-01716402116402810.1109/ACCESS.2019.29521558894083A Family of Multi-Parameterized Proximal Point AlgorithmsJianchao Bai0Ke Guo1https://orcid.org/0000-0002-7459-0715Xiaokai Chang2Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an, ChinaSchool of Mathematics and Information, China West Normal University, Nanchong, ChinaSchool of Science, Lanzhou University of Technology, Lanzhou, ChinaIn this paper, a multi-parameterized proximal point algorithm combining with a relaxation step is developed for solving convex minimization problem subject to linear constraints. We show its global convergence and sublinear convergence rate from the prospective of variational inequality. Preliminary numerical experiments on testing a sparse minimization problem from signal processing indicate that the proposed algorithm performs better than some well-established methods.https://ieeexplore.ieee.org/document/8894083/Convex optimizationproximal point algorithmcomplexitysignal processing |
| spellingShingle | Jianchao Bai Ke Guo Xiaokai Chang A Family of Multi-Parameterized Proximal Point Algorithms IEEE Access Convex optimization proximal point algorithm complexity signal processing |
| title | A Family of Multi-Parameterized Proximal Point Algorithms |
| title_full | A Family of Multi-Parameterized Proximal Point Algorithms |
| title_fullStr | A Family of Multi-Parameterized Proximal Point Algorithms |
| title_full_unstemmed | A Family of Multi-Parameterized Proximal Point Algorithms |
| title_short | A Family of Multi-Parameterized Proximal Point Algorithms |
| title_sort | family of multi parameterized proximal point algorithms |
| topic | Convex optimization proximal point algorithm complexity signal processing |
| url | https://ieeexplore.ieee.org/document/8894083/ |
| work_keys_str_mv | AT jianchaobai afamilyofmultiparameterizedproximalpointalgorithms AT keguo afamilyofmultiparameterizedproximalpointalgorithms AT xiaokaichang afamilyofmultiparameterizedproximalpointalgorithms AT jianchaobai familyofmultiparameterizedproximalpointalgorithms AT keguo familyofmultiparameterizedproximalpointalgorithms AT xiaokaichang familyofmultiparameterizedproximalpointalgorithms |