TY - JOUR
T1 - A Block-Coordinate Descent Method for Linearly Constrained Minimization Problem
AU - Liu , Xuefang
AU - Peng , Zheng
JO - Annals of Applied Mathematics
VL - 2
SP - 138
EP - 152
PY - 2022
DA - 2022/06
SN - 34
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/aam/20568.html
KW - linearly constrained optimization, block coordinate descent,
Gauss-Seidel fashion.
AB - <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>