  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.
