A parameterized proximal point algorithm for separable convex optimization

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, wheret denotes the...

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Bibliographic Details
Published in:arXiv.org
Main Authors: Bai, Jianchao, Zhang, Hongchao, Li, Jicheng
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 10.12.2018
Subjects:
Algorithms
Computational geometry
Convergence
Convexity
Iterative methods
Mathematical programming
Optimization
Parameterization
State of the art
ISSN:2331-8422
Online Access:Get full text
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Summary: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, wheret 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.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.1812.03759