A cyclic block coordinate descent method with generalized gradient projections

Silvia Bonettini, Marco Prato and Simone Rebegoldi

Applied Mathematics and Computation, 2016, vol. 286, issue C, 288-300

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.

Keywords: Constrained optimization; Gradient projection methods; Alternating algorithms; Nonconvex optimization (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:286:y:2016:i:c:p:288-300

DOI: 10.1016/j.amc.2016.04.031

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