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A family of multi-parameterized proximal point algorithms
Jianchao Bai(bjc1987 Abstract: In this paper, a multi-parameterized proximal point algorithm combining with a relaxation step is developed for solving convex minimization problem subject to linear constraints. We show its global convergence and sublinear convergence rate from the prospective of variational inequality. Preliminary numerical experiments on testing a sparse minimization problem from signal processing indicate that the proposed algorithm performs better than some well-established methods. Keywords: Convex optimization, proximal point algorithm, complexity, signal processing Category 1: Convex and Nonsmooth Optimization (Convex Optimization ) Citation: Download: [PDF] Entry Submitted: 07/09/2019 Modify/Update this entry | ||
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