Year: 2020
Author: Bingsheng He
Analysis in Theory and Applications, Vol. 36 (2020), Iss. 3 : pp. 262–282
Abstract
It is well recognized the convenience of converting the linearly constrained convex optimization problems to a monotone variational inequality. Recently, we have proposed a unified algorithmic framework which can guide us to construct the solution methods for solving these monotone variational inequalities. In this work, we revisit two full Jacobian decomposition of the augmented Lagrangian methods for separable convex programming which we have studied a few years ago. In particular, exploiting this framework, we are able to give a very clear and elementary proof of the convergence of these solution methods.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/ata.OA-SU13
Analysis in Theory and Applications, Vol. 36 (2020), Iss. 3 : pp. 262–282
Published online: 2020-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 21
Keywords: Convex programming augmented Lagrangian method multi-block separable model Jacobian splitting unified algorithmic framework.
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