Adaptive First-Order Methods for General Sparse Inverse Covariance Selection
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Publication:3053132
DOI10.1137/080742531zbMath1206.90115arXiv0904.0688OpenAlexW2131237233MaRDI QIDQ3053132
Publication date: 4 November 2010
Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/0904.0688
Numerical mathematical programming methods (65K05) Semidefinite programming (90C22) Convex programming (90C25) Minimax problems in mathematical programming (90C47) Analysis of variance and covariance (ANOVA) (62J10)
Related Items (12)
On how to solve large-scale log-determinant optimization problems ⋮ Ridge estimation of inverse covariance matrices from high-dimensional data ⋮ An inexact interior point method for \(L_{1}\)-regularized sparse covariance selection ⋮ Randomized Block Proximal Damped Newton Method for Composite Self-Concordant Minimization ⋮ A block coordinate gradient descent method for regularized convex separable optimization and covariance selection ⋮ On the existence of the weighted bridge penalized Gaussian likelihood precision matrix estimator ⋮ Positive-Definite ℓ1-Penalized Estimation of Large Covariance Matrices ⋮ A dual spectral projected gradient method for log-determinant semidefinite problems ⋮ An efficient algorithm for sparse inverse covariance matrix estimation based on dual formulation ⋮ Sparse estimation of high-dimensional inverse covariance matrices with explicit eigenvalue constraints ⋮ Unnamed Item ⋮ Fused Multiple Graphical Lasso
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