Publications associées à A Newton Frank-Wolfe method for constrained self-concordant 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

A Newton Frank-Wolfe method for constrained self-concordant minimization

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An optimization-based multiscale coupling method

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

Scalable Convex Methods for Phase Retrieval

Composite Self-Concordant Minimization

Estimating the ice thickness of shallow glaciers from surface topography and mass-balance data with a shape optimization algorithm

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

Image reconstruction of non-planar scenes from compressed multi-view measurements

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

Process Design and Optimization (MLS-S03): Model Identification by Gradient Methods

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

An optimization-based multiscale coupling method

Estimating the ice thickness of shallow glaciers from surface topography and mass-balance data with a shape optimization algorithm

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

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

Composite Self-Concordant Minimization

Image reconstruction of non-planar scenes from compressed multi-view measurements

Scalable Convex Methods for Phase Retrieval

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

Process Design and Optimization (MLS-S03): Model Identification by Gradient Methods

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