First-Order Methods for Sparse Covariance Selection

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Authors: A. d'Aspremont, O. Banerjee, and L. El Ghaoui.

Status: SIAM. J. Matrix Anal. & Appl. 30, 56 (2008).

Abstract: 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.

Related entries:
- Preprint on arXiv.

Code: COVSEL.

Bibtex reference:

@article{ABE:08,
	Author = {A. {d'Aspremont} and O. Banerjee and L. {El Ghaoui}},
	Journal = {SIAM. J. Matrix Anal. \& Appl.},
	Number = {56},
	Title = {First-Order Methods for Sparse Covariance Selection},
	Volume = {30},
	Year = {2008}}