A cyclic block coordinate descent method with generalized gradient projections

作者：Bonettini Silvia; Prato Marco^*; Rebegoldi Simone

来源：Applied Mathematics and Computation, 2016, 286: 288-300.

DOI：10.1016/j.amc.2016.04.031

• Constrained optimization
• Gradient projection methods
• Alternating algorithms
• Nonconvex optimization

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.

• 出版日期2016-8-5