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• W2754227567 abstract "In this paper, we develop a parameterized proximal point algorithm (P-PPA) for solving a class of separable convex programming problems subject to linear and convex constraints. The proposed algorithm is provable to be globally convergent with a worst-case O(1 / t) convergence rate, where t denotes the iteration number. By properly choosing the algorithm parameters, numerical experiments on solving a sparse optimization problem arising from statistical learning show that our P-PPA could perform significantly better than other state-of-the-art methods, such as the alternating direction method of multipliers and the relaxed proximal point algorithm." @default.
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• W2754227567 date "2017-09-12" @default.
• W2754227567 modified "2023-10-18" @default.
• W2754227567 title "A parameterized proximal point algorithm for separable convex optimization" @default.
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• W2754227567 doi "https://doi.org/10.1007/s11590-017-1195-9" @default.
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