期刊
SIAM JOURNAL ON IMAGING SCIENCES
卷 5, 期 1, 页码 119-149出版社
SIAM PUBLICATIONS
DOI: 10.1137/100814494
关键词
saddle-point problem; total variation; image restoration; primal-dual method; contraction method; proximal point algorithm
类别
资金
- National Natural Science Foundation of China [10971095, 91130007]
- Ministry of Education of China [20110091110004, 708044]
- Hong Kong General Research Fund [203311]
Recently, some primal-dual algorithms have been proposed for solving a saddle-point problem, with particular applications in the area of total variation image restoration. This paper focuses on the convergence analysis of these primal-dual algorithms and shows that their involved parameters (including step sizes) can be significantly enlarged if some simple correction steps are supplemented. Some new primal-dual-based methods are thus proposed for solving the saddle-point problem. We show that these new methods are of the contraction type: the iterative sequences generated by these new methods are contractive with respect to the solution set of the saddle-point problem. The global convergence of these new methods thus can be obtained within the analytic framework of contraction-type methods. The novel study on these primal-dual algorithms from the perspective of contraction methods substantially simplifies existing convergence analysis. Finally, we show the efficiency of the new methods numerically.
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