Sparse Inverse Covariance Estimation for Chordal Structures

  title={Sparse Inverse Covariance Estimation for Chordal Structures},
  author={S. Fattahi and Richard Y. Zhang and S. Sojoudi},
  journal={2018 European Control Conference (ECC)},
In this paper, we consider the Graphical Lasso (GL), a popular optimization problem for learning the sparse representations of high-dimensional datasets, which is well-known to be computationally expensive for large-scale problems. Recently, we have shown that the sparsity pattern of the optimal solution of GL is equivalent to the one obtained from simply thresholding the sample covariance matrix, for sparse graphs under different conditions. We have also derived a closed-form solution that is… Expand
Linear-Time Algorithm for Learning Large-Scale Sparse Graphical Models
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
Markov Random Fields for Collaborative Filtering
  • H. Steck
  • Computer Science, Mathematics
  • NeurIPS
  • 2019


Linear-Time Algorithm for Learning Large-Scale Sparse Graphical Models
Equivalence of graphical lasso and thresholding for sparse graphs
  • S. Sojoudi
  • Mathematics, Computer Science
  • 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
  • 2014
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QUIC: quadratic approximation for sparse inverse covariance estimation
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
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New Insights and Faster Computations for the Graphical Lasso
Chordal Graphs and Semidefinite Optimization