Study on the Splitting Methods for Separable Convex Optimization in a Unified Algorithmic Framework

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.

Author Details

Bingsheng He

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