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

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

8 January 2013

Anastasios Kyrillidis

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

6 / 6 papers shown

Title
Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity Jian-Feng Cai José Vinícius de Miranda Cardoso Daniel P. Palomar Jiaxi Ying 35 10 0 03 Dec 2021
$Does the $\ell_1$-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models?$ Does the $\ell_1$ -norm Learn a Sparse Graph under Laplacian Constrained Graphical Models? Jiaxi Ying J. Cardoso Daniel P. Palomar 18 10 0 26 Jun 2020
Self-Concordant Analysis of Frank-Wolfe Algorithms Pavel Dvurechensky P. Ostroukhov K. Safin Shimrit Shtern Mathias Staudigl 19 24 0 11 Feb 2020
Generalized Self-Concordant Functions: A Recipe for Newton-Type Methods Tianxiao Sun Quoc Tran-Dinh 19 60 0 14 Mar 2017
Dropping Convexity for Faster Semi-definite Optimization Srinadh Bhojanapalli Anastasios Kyrillidis Sujay Sanghavi 27 172 0 14 Sep 2015
Composite Self-Concordant Minimization Quoc Tran-Dinh Anastasios Kyrillidis V. Cevher 43 94 0 13 Aug 2013