A Line-Search-Based Partial Proximal Alternating Directions Method for Separable Convex Optimization
| Autor: | Yu-hua Zeng, Yu-fei Yang, Zheng Peng |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Journal of Applied Mathematics, Vol 2014 (2014) |
| Druh dokumentu: | article |
| ISSN: | 1110-757X 1687-0042 |
| DOI: | 10.1155/2014/540450 |
| Popis: | We propose an appealing line-search-based partial proximal alternating directions (LSPPAD) method for solving a class of separable convex optimization problems. These problems under consideration are common in practice. The proposed method solves two subproblems at each iteration: one is solved by a proximal point method, while the proximal term is absent from the other. Both subproblems admit inexact solutions. A line search technique is used to guarantee the convergence. The convergence of the LSPPAD method is established under some suitable conditions. The advantage of the proposed method is that it provides the tractability of the subproblem in which the proximal term is absent. Numerical tests show that the LSPPAD method has better performance compared with the existing alternating projection based prediction-correction (APBPC) method if both are employed to solve the described problem. |
| Databáze: | Directory of Open Access Journals |
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