R. Tibshirani, Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society, Series B, vol.58, pp.267-288, 1994.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, 2006.
DOI : 10.1198/016214502753479356

S. Chen, D. Donoho, and M. Saunders, Atomic Decomposition by Basis Pursuit, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.33-61, 1998.
DOI : 10.1137/S1064827596304010

M. Elad, J. Starck, P. Querre, and D. Donoho, Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA), Applied and Computational Harmonic Analysis, vol.19, issue.3, pp.340-358, 2005.
DOI : 10.1016/j.acha.2005.03.005

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, Robust face recognition via sparse representation, IEEE Trans. Pattern Anal. Mach. Intell, vol.31, issue.2, 2009.

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009.
DOI : 10.1137/080716542

P. L. Combettes and V. R. Wajs, Signal Recovery by Proximal Forward-Backward Splitting, Multiscale Modeling & Simulation, vol.4, issue.4, pp.1168-1200, 2005.
DOI : 10.1137/050626090

URL : https://hal.archives-ouvertes.fr/hal-00017649

I. Daubechies, M. Defrise, and C. Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Communications on Pure and Applied Mathematics, vol.58, issue.11, pp.1413-1457, 2004.
DOI : 10.1002/cpa.20042

S. J. Wright, R. D. Nowak, and M. A. Figueiredo, Sparse Reconstruction by Separable Approximation, IEEE Transactions on Signal Processing, vol.57, issue.7, pp.2479-2493, 2009.
DOI : 10.1109/TSP.2009.2016892

H. U. Arrow and L. Hurwicz, Studies in linear and non-linear programming, With contributions by Hollis B. Chenery [and others], 1964.

A. Chambolle and T. Pock, A First-Order Primal-Dual Algorithm for Convex Problems with??Applications to Imaging, Journal of Mathematical Imaging and Vision, vol.60, issue.5, pp.120-145, 2011.
DOI : 10.1007/s10851-010-0251-1

URL : https://hal.archives-ouvertes.fr/hal-00490826

Y. Nesterov, A method of solving a convex programming problem with convergence rate o (1/k2), Soviet Mathematics Doklady, vol.27, issue.2, pp.372-376, 1983.

S. Boyd and L. Vandenberghe, Convex Optimization, 2004.

L. Dai and K. Pelckmans, An ellipsoid based, two-stage screening test for BPDN, Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp.654-658, 2012.

L. Ghaoui, V. Viallon, and T. Rabbani, Safe Feature Elimination in Sparse Supervised Learning, EECS Department, 2010.

R. Tibshirani, J. Bien, J. Friedman, T. Hastie, N. Simon et al., Strong rules for discarding predictors in lasso-type problems, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.67, issue.2, pp.245-266, 2012.
DOI : 10.1111/j.1467-9868.2011.01004.x

J. Wang, B. Lin, P. Gong, P. Wonka, and J. Ye, Lasso Screening Rules via Dual Polytope Projection, pp.1-17, 2012.

Z. J. Xiang, H. Xu, and P. J. Ramadge, Learning sparse representations of high dimensional data on large scale dictionaries, Advances in Neural Information Processing Systems, pp.900-908, 2011.

Z. J. Xiang and P. J. Ramadge, Fast lasso screening tests based on correlations, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2137-2140, 2012.
DOI : 10.1109/ICASSP.2012.6288334

R. J. Tibshirani, The lasso problem and uniqueness, Electronic Journal of Statistics, vol.7, issue.0, pp.1456-1490, 2013.
DOI : 10.1214/13-EJS815

A. Bonnefoy, V. Emiya, L. Ralaivola, and R. Gribonval, A Dynamic Screening Principle for the Lasso, Proc. of EUSIPCO, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00880787

J. M. Bioucas-dias and M. A. Figueiredo, A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration, IEEE Transactions on Image Processing, vol.16, issue.12, pp.2992-3004, 2007.
DOI : 10.1109/TIP.2007.909319

L. Jacob, G. Obozinski, and J. Vert, Group lasso with overlap and graph lasso, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.433-440, 2009.
DOI : 10.1145/1553374.1553431

E. Vincent, S. Araki, and P. Bofill, The 2008 Signal Separation Evaluation Campaign: A Community-Based Approach to Large-Scale Evaluation, Proc. Int. Conf. on Independent Component Analysis and Signal Separation, 2009.
DOI : 10.1109/TASL.2007.899176

URL : https://hal.archives-ouvertes.fr/inria-00544168

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE, 1998.
DOI : 10.1109/5.726791

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Annals of Statistics, vol.32, pp.407-499, 2004.

Y. Wang, Z. J. Xiang, and P. L. Ramadge, Lasso screening with a small regularization parameter, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.2013-3342
DOI : 10.1109/ICASSP.2013.6638277

S. G. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.
DOI : 10.1109/78.258082

J. A. Tropp, Greed is Good: Algorithmic Results for Sparse Approximation, IEEE Transactions on Information Theory, vol.50, issue.10, pp.2231-2242, 2004.
DOI : 10.1109/TIT.2004.834793