@Article{AAM-34-2,
author = {Xuefang, Liu and Zheng, Peng},
title = {A Block-Coordinate Descent Method for Linearly Constrained Minimization Problem},
journal = {Annals of Applied Mathematics},
year = {2018},
volume = {34},
number = {2},
pages = {138--152},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {},
doi = {https://doi.org/2018-AAM-20568},
url = {https://global-sci.com/article/72712/a-block-coordinate-descent-method-for-linearly-constrained-minimization-problem}
}