We consider coordinate descent methods on convex quadratic problems, in which exact line searches are performed at each iteration. (This algorithm is identical to Gauss-Seidel on the equivalent symmetric positive definite linear system.) We describe a class of convex quadratic problems for which the random-permutations version of cyclic coordinate descent (RPCD) outperforms the standard cyclic ...

In this paper we study two types of descent in the category Berkovich analytic spaces: flat and with respect to an extension ground field. Quite surprisingly, deepest results direction seem be second type, including properties being a good space morphism without boundary.