A customized proximal point algorithm for convex minimization with linear constraints
作者： Bingsheng He , Xiaoming Yuan , Wenxing Zhang
DOI: 10.1007/S10589-013-9564-5
关键词: Metric (mathematics) 、 Image processing 、 Mathematics 、 Mathematical optimization 、 Benchmark (computing) 、 Algorithm 、 Proximal Gradient Methods 、 Resolvent operator 、 Augmented Lagrangian method 、 Proximal point 、 Convex optimization
摘要: This paper demonstrates a customized application of the classical proximal point algorithm (PPA) to convex minimization problem with linear constraints. We show that if parameter in metric form is chosen appropriately, PPA could be effective exploit simplicity objective function. The resulting subproblems easier than those augmented Lagrangian method (ALM), benchmark for model under our consideration. efficiency demonstrated by some image processing problems.
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