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• W1566327188 abstract "We propose an algorithmic framework for convex minimization problems of composite functions with two terms: a self-concordant part and a possibly nonsmooth regularization part. Our method is a new proximal Newton algorithm with local quadratic convergence rate. As a specific problem instance, we consider sparse precision matrix estimation problems in graph learning. Via a careful dual formulation and a novel analytic stepsize selection, we instantiate an algorithm within our framework for graph learning that avoids Cholesky decompositions and matrix inversions, making it attractive for parallel and distributed implementations." @default.
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• W1566327188 date "2013-06-16" @default.
• W1566327188 modified "2023-10-02" @default.
• W1566327188 title "A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions" @default.
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