Publications associées à Barrier Smoothing for Nonsmooth Convex Minimization

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Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Volkan Cevher, Anastasios Kyrillidis

The convex

\ell_1

-regularized

\log\det

divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the

\ell_1

-regularization is nonsmooth, the

\log\det

objective is n ...

2013

Barrier Smoothing for Nonsmooth Convex Minimization

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A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Composite Self-Concordant Minimization

Path-following gradient-based decomposition algorithms for separable convex optimization

Scalable sparse covariance estimation via self-concordance

A Primal-Dual Algorithmic Framework for Constrained Convex Minimization

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Constrained convex minimization via model-based excessive gap

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

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Inner approximations of the region of attraction for polynomial dynamical systems

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

A Primal-Dual Algorithmic Framework for Constrained Convex Minimization

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Constrained convex minimization via model-based excessive gap

Path-following gradient-based decomposition algorithms for separable convex optimization

Composite Self-Concordant Minimization

Scalable sparse covariance estimation via self-concordance

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Inner approximations of the region of attraction for polynomial dynamical systems

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