First-order methods for sparse covariance selection

作     者：D'Aspremont, Alexandre Banerjee, Onureena El Ghaoui, Laurent 

作者机构：Princeton Univ ORFE Dept Princeton NJ 08544 USA Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA 

出 版 物：《SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS》 (工业与应用数学会矩阵分析和应用杂志)

年 卷 期：2008年第30卷第1期

页      面：56-66页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：National Science Foundation  NSF  (0625352) 

主　　题：covariance selection semidefinite programming coordinate descent 

摘      要：Given a sample covariance matrix, we solve a maximum likelihood problem penalized by the number of nonzero coefficients in the inverse covariance matrix. Our objective is to find a sparse representation of the sample data and to highlight conditional independence relationships between the sample variables. We first formulate a convex relaxation of this combinatorial problem, we then detail two efficient first-order algorithms with low memory requirements to solve large-scale, dense problem instances.