Year: 2018
Author: Xuefang Liu, Zheng Peng
Annals of Applied Mathematics, Vol. 34 (2018), Iss. 2 : pp. 138–152
Abstract
In this paper, a block coordinate descent method is developed to solve a linearly constrained separable convex optimization problem. The proposed method divides the decision variable into a few blocks based on certain rules. Then the candidate solution is iteratively obtained by updating one block at each iteration. The problem, whether or not there are overlapping regions between two immediately adjacent blocks, is investigated. The global convergence of the proposed method is established under some suitable assumptions. Numerical results show that the proposed method is effective compared with some “full-type” methods.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2018-AAM-20568
Annals of Applied Mathematics, Vol. 34 (2018), Iss. 2 : pp. 138–152
Published online: 2018-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 15
Keywords: linearly constrained optimization block coordinate descent Gauss-Seidel fashion.