Publications associées à Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

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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

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

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Chattez avec Graph Search

Barrier Smoothing for Nonsmooth Convex Minimization

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

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

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Towards computational complexity certification for constrained mpc based on lagrange relaxation and the fast gradient method

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Optimization Problem Coupled with Differential Equations: A Numerical Algorithm Mixing an Interior-Point Method and Event Detection

Numerical analysis of optimization-constrained differential equations

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

Barrier Smoothing for Nonsmooth Convex Minimization