Year: 2009
Numerical Mathematics: Theory, Methods and Applications, Vol. 2 (2009), Iss. 4 : pp. 377–402
Abstract
This paper presents a coordinate gradient descent approach for minimizing the sum of a smooth function and a nonseparable convex function. We find a search direction by solving a subproblem obtained by a second-order approximation of the smooth function and adding a separable convex function. Under a local Lipschitzian error bound assumption, we show that the algorithm possesses global and local linear convergence properties. We also give some numerical tests (including image recovery examples) to illustrate the efficiency of the proposed method.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/nmtma.2009.m9002s
Numerical Mathematics: Theory, Methods and Applications, Vol. 2 (2009), Iss. 4 : pp. 377–402
Published online: 2009-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 26
Keywords: Coordinate descent global convergence linear convergence rate.
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