BigDataFr recommends: A Flexible Coordinate Descent Method for Big Data Applications

‘In this paper we present a novel randomized block coordinate descent method for the minimization of a convex composite objective function. The method uses (approximate) partial second-order (curvature) information, so that the algorithm performance is more robust when applied to highly nonseparable or ill conditioned problems. We call the method Flexible Coordinate Descent (FCD).

At each iteration of FCD, a block of coordinates is sampled randomly, a quadratic model is formed about that block and the model is minimized approximately/inexactly to determine the search direction. […]’

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By Kimon Fountoulakis, Rachael Tappenden
Source: arxiv.org