I. E. Auger and C. E. Lawrence, Algorithms for the optimal identification of segment neighborhoods, Bulletin of Mathematical Biology, vol.25, issue.1, pp.39-54, 1989.
DOI : 10.1007/978-1-4612-6137-7

H. H. Bauschke and P. L. Combettes, Convex analysis and monotone operator theory in Hilbert spaces, 2011.
DOI : 10.1007/978-3-319-48311-5
URL : https://hal.archives-ouvertes.fr/hal-00643354

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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.231.3271

P. Bellec, G. Lecué, and A. B. Tsybakov, Slope meets lasso: improved oracle bounds and optimality. arXiv preprint, 2016.

A. Belloni, V. Chernozhukov, and L. Wang, Square-root lasso: pivotal recovery of sparse signals via conic programming, Biometrika, vol.98, issue.4, pp.791-806, 2011.
DOI : 10.1093/biomet/asr043
URL : http://arxiv.org/abs/1009.5689

M. J. Best and N. Chakravarti, Active set algorithms for isotonic regression; A unifying framework, Mathematical Programming, vol.73, issue.1-3, pp.425-439, 1990.
DOI : 10.1007/BF01580873

R. Bhatia, Matrix analysis, volume 169 of Graduate Texts in Mathematics, 1997.

P. J. Bickel, Y. Ritov, and A. B. Tsybakov, Simultaneous analysis of Lasso and Dantzig selector, The Annals of Statistics, vol.37, issue.4, pp.1705-1732, 2009.
DOI : 10.1214/08-AOS620
URL : https://hal.archives-ouvertes.fr/hal-00401585

G. Boeing, OSMnx: New Methods for Acquiring, Constructing, Analyzing , and Visualizing Complex Street Networks. ArXiv e-prints ArXiv:1611, 1890.
DOI : 10.2139/ssrn.2865501
URL : http://arxiv.org/abs/1611.01890

M. Bogdan, E. Van-den-berg, C. Sabatti, W. Su, and E. J. Candès, SLOPE?Adaptive variable selection via convex optimization, The Annals of Applied Statistics, vol.9, issue.3, p.1103, 2015.
DOI : 10.1214/15-AOAS842SUPP
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4689150

S. Boucheron, G. Lugosi, and P. Massart, Concentration inequalities: A nonasymptotic theory of independence, 2013.
DOI : 10.1093/acprof:oso/9780199535255.001.0001
URL : https://hal.archives-ouvertes.fr/hal-00794821

P. L. Combettes and J. Pesquet, Proximal Splitting Methods in Signal Processing, Springer Optim. Appl, vol.49, pp.185-212, 2011.
DOI : 10.1007/978-1-4419-9569-8_10
URL : https://hal.archives-ouvertes.fr/hal-00643807

A. S. Dalalyan, M. Hebiri, and J. Lederer, On the prediction performance of the Lasso, Bernoulli, vol.23, issue.1, pp.552-581, 2017.
DOI : 10.3150/15-BEJ756

C. Deledalle, N. Papadakis, J. Salmon, and S. Vaiter, CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration, SIAM Journal on Imaging Sciences, vol.10, issue.1, pp.243-284, 2017.
DOI : 10.1137/16M1080318
URL : https://hal.archives-ouvertes.fr/hal-01534202

M. Elad, P. Milanfar, and R. Rubinstein, Analysis versus synthesis in signal priors, Inverse Problems, vol.23, issue.3, pp.947-968, 2007.
DOI : 10.1088/0266-5611/23/3/007
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.183.1395

Z. Fan and L. Guan, 0 -estimation of piecewise-constant signals on graphs. ArXiv e-prints ArXiv:1703, 1421.

J. Hütter and P. Rigollet, Optimal rates for total variation denoising. ArXiv e-prints ArXiv:1603.09388, 2016.

E. Mammen and S. Van-de-geer, Locally adaptive regression splines, The Annals of Statistics, vol.25, issue.1, pp.387-413, 1997.
DOI : 10.1214/aos/1034276635
URL : http://archiv.ub.uni-heidelberg.de/volltextserver/21350/1/beitrag.10.pdf

E. Ndiaye, O. Fercoq, A. Gramfort, V. Lecì, and J. Salmon, Efficient smoothed concomitant lasso estimation for high dimensional regression, NCMIP, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01404966

A. Y. Ng, M. I. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm, NIPS, pp.849-856, 2001.

A. B. Owen, A robust hybrid of lasso and ridge regression, Contemporary Mathematics, vol.443, pp.59-72, 2007.
DOI : 10.1090/conm/443/08555

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, J. Mach. Learn. Res, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, pp.259-268, 1992.
DOI : 10.1016/0167-2789(92)90242-F

V. Sadhanala, Y. Wang, and R. J. Tibshirani, Total variation classes beyond 1d: Minimax rates, and the limitations of linear smoothers, NIPS, pp.3513-3521, 2016.

J. Sharpnack, A. Singh, and A. Rinaldo, Sparsistency of the edge lasso over graphs, AISTATS, pp.1028-1036, 2012.

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell, vol.22, issue.8, pp.888-905, 2000.

W. Su and E. J. Candès, SLOPE is adaptive to unknown sparsity and asymptotically minimax, The Annals of Statistics, vol.44, issue.3, pp.1038-1068, 2016.
DOI : 10.1214/15-AOS1397SUPP
URL : http://arxiv.org/abs/1503.08393

T. Sun and C. Zhang, Scaled sparse linear regression, Biometrika, vol.99, issue.4, pp.879-898, 2012.
DOI : 10.1093/biomet/ass043
URL : http://arxiv.org/abs/1104.4595

R. Tibshirani, M. A. Saunders, S. Rosset, J. Zhu, and K. Knight, Sparsity and smoothness via the fused lasso, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.99, issue.1, pp.91-108, 2005.
DOI : 10.1016/S0140-6736(02)07746-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.4573

S. Van-de-geer and P. Bühlmann, On the conditions used to prove oracle results for the Lasso, Electronic Journal of Statistics, vol.3, issue.0, pp.1360-1392, 2009.
DOI : 10.1214/09-EJS506

V. Viallon, S. Lambert-lacroix, H. Hoefling, and F. Picard, On the robustness of the generalized fused lasso to prior specifications, Statistics and Computing, vol.101, issue.476, pp.285-301, 2016.
DOI : 10.1198/016214506000000735
URL : https://hal.archives-ouvertes.fr/hal-01067903

U. and V. Luxburg, A tutorial on spectral clustering, Statistics and Computing, vol.21, issue.1, pp.395-416, 2007.
DOI : 10.1017/CBO9780511810633

D. J. Watts, Networks, Dynamics, and the Small?World Phenomenon, American Journal of Sociology, vol.105, issue.2, pp.493-527, 1999.
DOI : 10.1086/210318
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.78.4413

F. Ye and C. Zhang, Rate minimaxity of the lasso and dantzig selector for the lq loss in lr balls, J. Mach. Learn. Res, vol.11, pp.3519-3540, 2010.

X. Zeng and M. A. Figueiredo, The Ordered Weighted 1 Norm: Atomic Formulation, Projections, and Algorithms. ArXiv e-prints arXiv:1409, 2014.