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arXiv · 1502.06737

A cyclic block coordinate descent method with generalized gradient projections

Silvia Bonettini·Marco Prato·Simone Rebegoldi
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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.

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Silvia Bonettini, Marco Prato, Simone Rebegoldi. 2015-02-24. A cyclic block coordinate descent method with generalized gradient projections. https://doi.org/10.1016/j.amc.2016.04.031

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