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A customized proximal point algorithm for convex minimization with linear constraints |
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| Authors: | Bingsheng He Xiaoming Yuan Wenxing Zhang |
| Affiliation: | 1. International Center of Management Science and Engineering and Department of Mathematics, Nanjing University, Nanjing, 210093, China 2. Department of Mathematics, Hong Kong Baptist University, Hong Kong, China 3. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China |
| Abstract: | This paper demonstrates a customized application of the classical proximal point algorithm (PPA) to the convex minimization problem with linear constraints. We show that if the proximal parameter in metric form is chosen appropriately, the application of PPA could be effective to exploit the simplicity of the objective function. The resulting subproblems could be easier than those of the augmented Lagrangian method (ALM), a benchmark method for the model under our consideration. The efficiency of the customized application of PPA is demonstrated by some image processing problems. |
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