A. Aitken, On Bernoulli's numerical solution of algebraic equations, Proceedings of the Royal Society of Edinburgh, vol.46, pp.289-305, 1926.

D. G. Anderson, Iterative procedures for nonlinear integral equations, Journal of the ACM, 1965.

F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, Convex optimization with sparsity-inducing norms. Foundations and Trends in Machine Learning, vol.4, pp.1-106, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00937150

A. Beck, First-Order Methods in Optimization, 2017.

S. Behnel, R. Bradshaw, C. Citro, L. Dalcin, D. S. Seljebotn et al., The best of both worlds. Computing in Science Engineering, vol.13, pp.31-39, 2011.

A. Boisbunon, R. Flamary, and A. Rakotomamonjy, Active set strategy for high-dimensional non-convex sparse optimization problems, ICASSP, pp.1517-1521, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01025585

A. Bonnefoy, V. Emiya, L. Ralaivola, and R. Gribonval, A dynamic screening principle for the lasso, EUSIPCO, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00880787

R. P. Eddy, Extrapolating to the limit of a vector sequence, Information Linkage between Applied Mathematics and Industry, 1979.

L. E. Ghaoui, V. Viallon, and T. Rabbani, Safe feature elimination in sparse supervised learning, J. Pacific Optim, vol.8, issue.4, pp.667-698, 2012.

R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin, Liblinear: A library for large linear classification, J. Mach. Learn. Res, vol.9, pp.1871-1874, 2008.

J. Fan and J. Lv, Sure independence screening for ultrahigh dimensional feature space, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.70, issue.5, pp.849-911, 2008.

O. Fercoq, A. Gramfort, and J. Salmon, Mind the duality gap: safer rules for the lasso, ICML, 2015.

O. Fercoq and P. Richtárik, Accelerated, parallel and proximal coordinate descent, SIAM J. Optim, vol.25, issue.3, pp.1997-2013, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02287265

J. Friedman, T. J. Hastie, H. Höfling, and R. Tibshirani, Pathwise coordinate optimization, Ann. Appl. Stat, vol.1, issue.2, pp.302-332, 2007.

E. Hale, W. Yin, and Y. Zhang, Fixed-point continuation for 1-minimization: Methodology and convergence, SIAM J. Optim, vol.19, issue.3, pp.1107-1130, 2008.

C. Hsieh, M. Sustik, I. Dhillon, and P. Ravikumar, QUIC: Quadratic approximation for sparse inverse covariance estimation, J. Mach. Learn. Res, vol.15, pp.2911-2947, 2014.

T. B. Johnson and C. Guestrin, Blitz: A principled metaalgorithm for scaling sparse optimization, ICML, pp.1171-1179, 2015.

T. B. Johnson and C. Guestrin, A fast, principled working set algorithm for exploiting piecewise linear structure in convex problems, 2018.

K. Koh, S. Kim, and S. Boyd, An interior-point method for large-scale 1-regularized logistic regression, J. Mach. Learn. Res, vol.8, issue.8, pp.1519-1555, 2007.

J. Lee, Y. Sun, and M. Saunders, Proximal Newton-type methods for convex optimization, NIPS, 2012.

J. , Sparse coding for machine learning, image processing and computer vision, 2010.

M. Massias, A. Gramfort, and J. Salmon, From safe screening rules to working sets for faster lasso-type solvers, NIPS-OPT, 2017.

M. Massias, A. Gramfort, and J. Salmon, Celer: a fast solver for the Lasso with dual extrapolation, ICML, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01833398

E. Ndiaye, O. Fercoq, A. Gramfort, and J. Salmon, Gap safe screening rules for sparsity enforcing penalties, J. Mach. Learn. Res, vol.18, issue.128, pp.1-33, 2017.

G. Obozinski, B. Taskar, and M. I. Jordan, Joint covariate selection and subspace selection for multiple classification problems, Statistics and Computing, vol.20, issue.2, pp.231-252, 2010.

V. Roth and B. Fischer, The group-lasso for generalized linear models: uniqueness of solutions and efficient algorithms, ICML, pp.848-855, 2008.

M. De-santis, S. Lucidi, and F. Rinaldi, A fast active set block coordinate descent algorithm for 1-regularized least squares, SIAM J. Optim, vol.26, issue.1, pp.781-809, 2016.

K. Scheinberg and X. Tang, Complexity of inexact proximal Newton methods, 2013.

D. Scieur, Acceleration in Optimization, 2018.
URL : https://hal.archives-ouvertes.fr/tel-01887163

R. Tibshirani, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.58, issue.1, pp.267-288, 1996.

R. Tibshirani, J. Bien, J. Friedman, T. J. Hastie, N. Simon et al., Strong rules for discarding predictors in lasso-type problems, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.74, issue.2, pp.245-266, 2012.

R. J. Tibshirani, The lasso problem and uniqueness, Electron. J. Stat, vol.7, pp.1456-1490, 2013.

P. Tseng, Convergence of a block coordinate descent method for nondifferentiable minimization, J. Optim. Theory Appl, vol.109, issue.3, pp.475-494, 2001.

J. Wang, P. Wonka, and J. Ye, Lasso screening rules via dual polytope projection, 2012.

Z. J. Xiang, Y. Wang, and P. J. Ramadge, Screening tests for lasso problems, IEEE Trans. Pattern Anal. Mach. Intell, p.99, 2016.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.68, issue.1, pp.49-67, 2006.