@Article{EAJAM-2-326,
author = {Raymond H. Chan, Min Tao and Xiaoming Yuan},
title = {Linearized Alternating Direction Method of Multipliers for Constrained Linear Least-Squares Problem},
journal = {East Asian Journal on Applied Mathematics},
year = {2018},
volume = {2},
number = {4},
pages = {326--341},
abstract = {<p style="text-align: justify;">The alternating direction method of multipliers (ADMM) is applied to a constrained
linear least-squares problem, where the objective function is a sum of two
least-squares terms and there are box constraints. The original problem is decomposed
into two easier least-squares subproblems at each iteration, and to speed up the inner
iteration we linearize the relevant subproblem whenever it has no known closed-form
solution. We prove the convergence of the resulting algorithm, and apply it to solve
some image deblurring problems. Its efficiency is demonstrated, in comparison with
Newton-type methods.</p>},
issn = {2079-7370},
doi = {https://doi.org/10.4208/eajam.270812.161112a},
url = {http://global-sci.org/intro/article_detail/eajam/10880.html}
}