Convergence analysis of a parallel projection algorithm for solving convex feasibility problems
The convex feasibility problem (CFP) is a classical problem in nonlinear analysis. In this paper, we propose an inertial parallel projection algorithm for solving CFP. Different from the previous algorithms, the proposed method introduces a sequence of parameters and uses the information of last two...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
American Institute of Mathematical Sciences
2016
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| Online Access: | http://purl.org/au-research/grants/arc/DP160102819 http://hdl.handle.net/20.500.11937/52586 |
| _version_ | 1848758963215007744 |
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| author | Dang, Y. Meng, F. Sun, Jie |
| author_facet | Dang, Y. Meng, F. Sun, Jie |
| author_sort | Dang, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The convex feasibility problem (CFP) is a classical problem in nonlinear analysis. In this paper, we propose an inertial parallel projection algorithm for solving CFP. Different from the previous algorithms, the proposed method introduces a sequence of parameters and uses the information of last two iterations at each step. To prove its convergence in a simple way, we transform the parallel algorithm to a sequential one in a constructed product space. Preliminary experiments are conducted to demonstrate that the proposed approach converges faster than the general extrapolated algorithms. |
| first_indexed | 2025-11-14T09:52:20Z |
| format | Journal Article |
| id | curtin-20.500.11937-52586 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:52:20Z |
| publishDate | 2016 |
| publisher | American Institute of Mathematical Sciences |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-525862022-10-27T04:35:30Z Convergence analysis of a parallel projection algorithm for solving convex feasibility problems Dang, Y. Meng, F. Sun, Jie The convex feasibility problem (CFP) is a classical problem in nonlinear analysis. In this paper, we propose an inertial parallel projection algorithm for solving CFP. Different from the previous algorithms, the proposed method introduces a sequence of parameters and uses the information of last two iterations at each step. To prove its convergence in a simple way, we transform the parallel algorithm to a sequential one in a constructed product space. Preliminary experiments are conducted to demonstrate that the proposed approach converges faster than the general extrapolated algorithms. 2016 Journal Article http://hdl.handle.net/20.500.11937/52586 10.3934/naco.2016023 http://purl.org/au-research/grants/arc/DP160102819 American Institute of Mathematical Sciences unknown |
| spellingShingle | Dang, Y. Meng, F. Sun, Jie Convergence analysis of a parallel projection algorithm for solving convex feasibility problems |
| title | Convergence analysis of a parallel projection algorithm for solving convex feasibility problems |
| title_full | Convergence analysis of a parallel projection algorithm for solving convex feasibility problems |
| title_fullStr | Convergence analysis of a parallel projection algorithm for solving convex feasibility problems |
| title_full_unstemmed | Convergence analysis of a parallel projection algorithm for solving convex feasibility problems |
| title_short | Convergence analysis of a parallel projection algorithm for solving convex feasibility problems |
| title_sort | convergence analysis of a parallel projection algorithm for solving convex feasibility problems |
| url | http://purl.org/au-research/grants/arc/DP160102819 http://hdl.handle.net/20.500.11937/52586 |