F. Bach, Learning with Submodular Functions: A Convex Optimization Perspective, Foundations and Trends?? in Machine Learning, vol.6, issue.2-3, 2013.
DOI : 10.1561/2200000039

URL : https://hal.archives-ouvertes.fr/hal-00645271

H. H. Bauschke and J. M. Borwein, Dykstra???s Alternating Projection Algorithm for Two Sets, Journal of Approximation Theory, vol.79, issue.3, pp.418-443, 1994.
DOI : 10.1006/jath.1994.1136

H. H. Bauschke, J. M. Borwein, and A. S. Lewis, The method of cyclic projections for closed convex sets in Hilbert space, Contemporary Mathematics, vol.204, pp.1-38, 1997.
DOI : 10.1090/conm/204/02620

H. H. Bauschke, P. L. Combettes, and D. Luke, Finding best approximation pairs relative to two closed convex sets in Hilbert spaces, Journal of Approximation Theory, vol.127, issue.2, pp.178-192, 2004.
DOI : 10.1016/j.jat.2004.02.006

URL : https://hal.archives-ouvertes.fr/hal-00017939

D. P. Bertsekas, Nonlinear programming, Athena Scientific, 1999.

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

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1137, 2004.
DOI : 10.1109/TPAMI.2004.60

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001.
DOI : 10.1109/34.969114

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.6806

D. Chakrabarty, P. Jain, and P. Kothari, Provable submodular minimization using Wolfe's algorithm, Advances in Neural Information Processing Systems, 2014.

A. Chambolle and J. Darbon, On Total Variation Minimization and Surface Evolution Using Parametric Maximum Flows, International Journal of Computer Vision, vol.40, issue.9, pp.288-307, 2009.
DOI : 10.1007/s11263-009-0238-9

A. Chambolle and T. Pock, A remark on accelerated block coordinate descent for computing the proximity operators of a sum of convex functions, SMAI Journal of Computational Mathematics, vol.1
DOI : 10.5802/smai-jcm.3

URL : https://hal.archives-ouvertes.fr/hal-01099182

S. Fujishige, Lexicographically Optimal Base of a Polymatroid with Respect to a Weight Vector, Mathematics of Operations Research, vol.5, issue.2, pp.186-196, 1980.
DOI : 10.1287/moor.5.2.186

S. Fujishige, Submodular Functions and Optimization, 2005.

N. Gaffke and R. Mathar, A cyclic projection algorithm via duality, Metrika, vol.68, issue.1, pp.29-54, 1989.
DOI : 10.1007/BF02614077

D. Goldfarb and W. Yin, Parametric Maximum Flow Algorithms for Fast Total Variation Minimization, SIAM Journal on Scientific Computing, vol.31, issue.5, pp.3712-3743, 2009.
DOI : 10.1137/070706318

H. Groenevelt, Two algorithms for maximizing a separable concave function over a polymatroid feasible region, European Journal of Operational Research, vol.54, issue.2, pp.227-236, 1991.
DOI : 10.1016/0377-2217(91)90300-K

S. Jegelka, F. Bach, and S. Sra, Reflection methods for user-friendly submodular optimization, Advances in Neural Information Processing Systems, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00905258

V. Kolmogorov, Minimizing a sum of submodular functions, Discrete Applied Mathematics, vol.160, issue.15, p.2012
DOI : 10.1016/j.dam.2012.05.025

N. Komodakis, N. Paragios, and G. Tziritas, MRF Energy Minimization and Beyond via Dual Decomposition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.3, pp.531-552, 2011.
DOI : 10.1109/TPAMI.2010.108

URL : https://hal.archives-ouvertes.fr/hal-00856311

A. Krause and C. Guestrin, Submodularity and its applications in optimized information gathering, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.4, 2011.
DOI : 10.1145/1989734.1989736

K. S. Kumar, A. Barbero, S. Jegelka, S. Sra, and F. Bach, Convex optimization for parallel energy minimization
URL : https://hal.archives-ouvertes.fr/hal-01123492

L. Ladicky, C. Russell, P. Kohli, and P. H. Torr, Graph Cut Based Inference with Co-occurrence Statistics, Proceedings of the 11th European Conference on Computer Vision, 2010.
DOI : 10.1007/978-3-642-15555-0_18

L. Landrieu and G. Obozinski, Cut Pursuit: fast algorithms to learn piecewise constant functions, Proceedings of Artificial Intelligence and Statistics, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01306786

H. Lin and J. Bilmes, A class of submodular functions for document summarization, Proceedings of NAACL/HLT, 2011.

L. Lovász, Submodular functions and convexity Mathematical programming: the state of the art, pp.235-257, 1982.

J. Nocedal and S. J. Wright, Numerical optimization. Springer Series in Operations Research and Financial Engineering, 2006.

X. Shusheng, Estimation of the convergence rate of Dykstra???s cyclic projections algorithm in polyhedral case, Acta Mathematicae Applicatae Sinica, vol.15, issue.2, pp.217-220, 2000.
DOI : 10.1007/BF02677683

P. Stobbe and A. Krause, Efficient minimization of decomposable submodular functions, Advances in Neural Information Processing Systems, 2010.

R. Tarjan, J. Ward, B. Zhang, Y. Zhou, and J. Mao, Balancing Applied to Maximum Network Flow Problems, European Symp. on Algorithms (ESA), pp.612-623, 2006.
DOI : 10.1007/11841036_55

N. Vishnoi, Lx = B -Laplacian Solvers and Their Algorithmic Applications, 2013.