Conference paper

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

We 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.


    • EPFL-CONF-183012

    Record created on 2013-01-08, modified on 2017-05-10

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