Application of the Strictly Contractive Peaceman-Rachford Splitting Method to Multi-Block Separable Convex Programming

DOI10.1007/978-3-319-41589-5_6zbMath1372.65172OpenAlexW2571436137MaRDI QIDQ5350478

Juwei Lu, Han Liu, Xiao-Ming Yuan, Bing-sheng He

Publication date: 1 September 2017

Published in: Splitting Methods in Communication, Imaging, Science, and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-41589-5_6

zbMATH Keywords

convergence numerical examples image processing convex minimization statistical learning proximal regularization strictly contractive Peaceman-Rachford splitting method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Learning and adaptive systems in artificial intelligence (68T05) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18)

Related Items (5)

A proximal Peaceman–Rachford splitting method for solving the multi-block separable convex minimization problems ⋮ A modified strictly contractive peaceman-Rachford splitting method for multi-block separable convex programming ⋮ A faster generalized ADMM-based algorithm using a sequential updating scheme with relaxed step sizes for multiple-block linearly constrained separable convex programming ⋮ Relaxed inertial proximal Peaceman-Rachford splitting method for separable convex programming ⋮ Block-wise Alternating Direction Method of Multipliers for Multiple-block Convex Programming and Beyond

This page was built for publication: Application of the Strictly Contractive Peaceman-Rachford Splitting Method to Multi-Block Separable Convex Programming