Tran Dinh, QuocKyrillidis, AnastasiosCevher, Volkan2013-01-082013-01-082013-01-082013https://infoscience.epfl.ch/handle/20.500.14299/87702We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local quadratic convergence rate. As a specific instance of our framework, we consider the sparse inverse covariance matrix estimation in graph learning problems. Via a careful dual formulation and a novel analytic step-size selection procedure, our approach for graph learning avoids Cholesky decompositions and matrix inversions in its iteration making it attractive for parallel and distributed implementations.text::conference output::conference proceedings::conference paper