J. C. Duchi, S. Gould, and D. Koller, Projected Subgradient Methods for Learning Sparse Gaussians, UAI 2008, Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence, pp.145-152, 2008.

S. Ma, L. Xue, and H. Zou, Alternating Direction Methods for Latent Variable Gaussian Graphical Model Selection, Neural Computation, vol.11, issue.1, pp.2172-2198, 2013.
DOI : 10.1007/s10915-011-9507-1

URL : http://arxiv.org/pdf/1206.1275.pdf

O. Banerjee, L. Ghaoui, and A. , Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data, J. Mach. Learn. Res, vol.9, pp.485-516, 2008.

V. Chandrasekaran, P. A. Parrilo, and A. S. Willsky, Latent variable graphical model selection via convex optimization, The Annals of Statistics, vol.40, issue.4, pp.1935-1967, 2012.
DOI : 10.1214/11-AOS949SUPP

URL : http://doi.org/10.1214/11-aos949

J. Guo, E. Levina, G. Michailidis, and J. Zhu, Joint estimation of multiple graphical models, Biometrika, vol.36, issue.1, 2011.
DOI : 10.1214/07-AOS507

URL : http://europepmc.org/articles/pmc3412604?pdf=render

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.94, issue.1, pp.432-441, 2008.
DOI : 10.1093/biomet/asm018

URL : https://academic.oup.com/biostatistics/article-pdf/9/3/432/17742149/kxm045.pdf

A. Dempster, Covariance Selection, Biometrics, vol.28, issue.1, pp.157-175, 1972.
DOI : 10.2307/2528966

X. Yuan, Alternating Direction Method for Covariance Selection Models, Journal of Scientific Computing, vol.94, issue.1, pp.261-273, 2012.
DOI : 10.1093/biomet/asm018

A. Aspremont, O. Banerjee, and L. Ghaoui, First-Order Methods for Sparse Covariance Selection, SIAM Journal on Matrix Analysis and Applications, vol.30, issue.1, pp.56-66, 2008.
DOI : 10.1137/060670985

C. Wang, D. Sun, and K. Toh, Solving Log-Determinant Optimization Problems by a Newton-CG Primal Proximal Point Algorithm, SIAM Journal on Optimization, vol.20, issue.6, pp.2994-3013, 2010.
DOI : 10.1137/090772514

N. Meinshausen and P. Bühlmann, High-dimensional graphs and variable selection with the Lasso, The Annals of Statistics, vol.34, issue.3, pp.1436-1462, 2006.
DOI : 10.1214/009053606000000281

URL : http://doi.org/10.1214/009053606000000281

P. Ravikumar, M. J. Wainwright, G. Raskutti, and B. Yu, High-dimensional covariance estimation by minimizing ???1-penalized log-determinant divergence, Electronic Journal of Statistics, vol.5, issue.0, pp.935-980, 2011.
DOI : 10.1214/11-EJS631

URL : http://doi.org/10.1214/11-ejs631

M. Yuan and Y. Lin, Model selection and estimation in the Gaussian graphical model, Biometrika, vol.94, issue.1, p.19, 2007.
DOI : 10.1093/biomet/asm018

M. S. Aslan, X. Chen, and H. Cheng, Analyzing and learning sparse and scale-free networks using Gaussian graphical models, International Journal of Data Science and Analytics, vol.31, issue.9, pp.99-109, 2016.
DOI : 10.1093/nar/gkg340

URL : https://link.springer.com/content/pdf/10.1007%2Fs41060-016-0009-y.pdf

S. Yang, Z. Lu, X. Shen, P. Wonka, and J. Ye, Fused Multiple Graphical Lasso, SIAM Journal on Optimization, vol.25, issue.2, pp.916-943, 2015.
DOI : 10.1137/130936397

URL : http://people.math.sfu.ca/%7Ezhaosong/ResearchPapers/FMGL.pdf

R. Mazumder and T. Hastie, The graphical lasso: New insights and alternatives, Electronic Journal of Statistics, vol.6, issue.0, pp.2125-2149, 2012.
DOI : 10.1214/12-EJS740

URL : http://doi.org/10.1214/12-ejs740

A. J. Rothman, P. J. Bickel, E. Levina, and J. Zhu, Sparse permutation invariant covariance estimation, Electronic Journal of Statistics, vol.2, issue.0, pp.494-515, 2008.
DOI : 10.1214/08-EJS176

URL : http://doi.org/10.1214/08-ejs176

T. Cai, W. Liu, and X. Luo, Minimization Approach to Sparse Precision Matrix Estimation, Journal of the American Statistical Association, vol.106, issue.494, pp.594-607, 2011.
DOI : 10.1198/jasa.2011.tm10155

URL : http://arxiv.org/pdf/1102.2233

Z. Lu, Smooth Optimization Approach for Sparse Covariance Selection, SIAM Journal on Optimization, vol.19, issue.4, pp.1807-1827, 2009.
DOI : 10.1137/070695915

URL : http://arxiv.org/pdf/0904.0687

Z. Lu, Adaptive First-Order Methods for General Sparse Inverse Covariance Selection, SIAM Journal on Matrix Analysis and Applications, vol.31, issue.4, pp.2000-2016, 2010.
DOI : 10.1137/080742531

URL : http://arxiv.org/pdf/0904.0688

Y. Nesterov, Smooth minimization of non-smooth functions, Mathematical Programming, vol.269, issue.1, pp.127-152, 2005.
DOI : 10.1007/s10107-004-0552-5

URL : http://www.core.ucl.ac.be/services/psfiles/dp03/dp2003-12.pdf

K. Scheinberg, S. Ma, and D. Goldfarb, Sparse inverse covariance selection via alternating linearization methods, Adv. Neural Inf. Proc. Sys. 23, pp.2101-2109, 2010.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Foundations and Trends?? in Machine Learning, vol.3, issue.1, pp.1-122, 2011.
DOI : 10.1561/2200000016

URL : http://www.nowpublishers.com/article/DownloadSummary/MAL-016

L. Li and K. C. Toh, An inexact interior point method for L 1-regularized sparse covariance selection, Mathematical Programming Computation, vol.99, issue.12, pp.291-315, 2010.
DOI : 10.1080/10556789908805762

URL : http://www.optimization-online.org/DB_FILE/2010/02/2550.pdf

Y. Sun, P. Babu, and D. P. Palomar, Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning, IEEE Transactions on Signal Processing, vol.65, issue.3, pp.794-816, 2017.
DOI : 10.1109/TSP.2016.2601299

M. E. Tipping, Sparse Bayesian learning and the relevance vector machine, J. Mach. Learn. Res, vol.1, pp.211-244, 2001.

D. P. Wipf and B. D. Rao, Sparse Bayesian Learning for Basis Selection, IEEE Transactions on Signal Processing, vol.52, issue.8, pp.2153-2164, 2004.
DOI : 10.1109/TSP.2004.831016

URL : http://dsp.rice.edu/files/cs/Rao8.pdf

J. R. Magnus and H. Neudecker, Matrix Differential Calculus with Applications in Statistics and Econometrics., Biometrics, vol.44, issue.4, 1999.
DOI : 10.2307/2531754

E. Chouzenoux and J. Pesquet, Convergence Rate Analysis of the Majorize???Minimize Subspace Algorithm, IEEE Signal Processing Letters, vol.23, issue.9, pp.1284-1288, 2016.
DOI : 10.1109/LSP.2016.2593589

URL : https://hal.archives-ouvertes.fr/hal-01373641

D. R. Hunter and K. Lange, A Tutorial on MM Algorithms, The American Statistician, vol.58, issue.1, pp.30-37, 2004.
DOI : 10.1198/0003130042836

M. W. Jacobson and J. A. Fessler, An Expanded Theoretical Treatment of Iteration-Dependent Majorize-Minimize Algorithms, IEEE Transactions on Image Processing, vol.16, issue.10, pp.2411-2422, 2007.
DOI : 10.1109/TIP.2007.904387

URL : http://europepmc.org/articles/pmc2750827?pdf=render

P. L. Lions and B. Mercier, Splitting Algorithms for the Sum of Two Nonlinear Operators, SIAM Journal on Numerical Analysis, vol.16, issue.6, pp.964-979, 1979.
DOI : 10.1137/0716071

P. L. Combettes and J. Pesquet, Proximal Splitting Methods in Signal Processing A Douglas-Rachford splitting approach to nonsmooth convex variational signal recovery, Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp.185-212, 2007.
DOI : 10.1109/jstsp.2007.910264

C. F. Wu, On the Convergence Properties of the EM Algorithm, The Annals of Statistics, vol.11, issue.1, pp.95-103, 1983.
DOI : 10.1214/aos/1176346060