Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4625747 | Applied Mathematics and Computation | 2016 | 13 Pages |
Abstract
The aim of this paper is to present the convergence analysis of a very general class of gradient projection methods for smooth, constrained, possibly nonconvex, optimization. The key features of these methods are the Armijo linesearch along a suitable descent direction and the non Euclidean metric employed to compute the gradient projection. We develop a very general framework from the point of view of block-coordinate descent methods, which are useful when the constraints are separable. In our numerical experiments we consider a large scale image restoration problem to illustrate the impact of the metric choice on the practical performances of the corresponding algorithm.
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Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Silvia Bonettini, Marco Prato, Simone Rebegoldi,