Linearized Alternating Direction Method of Multipliers for Constrained Linear Least-Squares Problem
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AbstractThe 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.
All Author(s) ListChan RH, Tao M, Yuan XM
Journal nameEAST ASIAN JOURNAL ON APPLIED MATHEMATICS
Year2012
Month11
Day1
Volume Number2
Issue Number4
PublisherGLOBAL SCIENCE PRESS
Pages326 - 341
ISSN2079-7362
eISSN2079-7370
LanguagesEnglish-United Kingdom
Keywordsalternating direction method of multipliers; image processing; Linear least-squares problems; linearization
Web of Science Subject CategoriesMathematics; Mathematics, Applied

Last updated on 2020-17-11 at 23:57