R. Baucke, A. Downward, and G. Zakeri, A deterministic algorithm for solving multistage stochastic programming problems. Optimization Online, 2017.

H. Heinz, . Bauschke, L. Patrick, and . Combettes, Convex analysis and monotone operator theory in Hilbert spaces, 2011.

R. Bellman, Dynamic Programming, 1957.

P. Dimitri and . Bertsekas, Dynamic programming and optimal control, 2005.

J. Borwein, S. Adrian, and . Lewis, Convex analysis and nonlinear optimization: theory and examples, 2010.

I. Dunning, J. Huchette, and M. Lubin, JuMP: A Modeling Language for Mathematical Optimization, SIAM Review, vol.59, issue.2, pp.295-320, 2017.
DOI : 10.1137/15M1020575
URL : http://arxiv.org/pdf/1508.01982

P. Girardeau, V. Leclere, B. Andrew, and . Philpott, On the Convergence of Decomposition Methods for Multistage Stochastic Convex Programs, Mathematics of Operations Research, vol.40, issue.1, pp.130-145, 2014.
DOI : 10.1287/moor.2014.0664
URL : https://hal.archives-ouvertes.fr/hal-01208295

V. Guigues, Convergence Analysis of Sampling-Based Decomposition Methods for Risk-Averse Multistage Stochastic Convex Programs, SIAM Journal on Optimization, vol.26, issue.4, pp.2468-2494, 2016.
DOI : 10.1137/140983136

V. Guigues, Dual Dynamic Programing with cut selection: Convergence proof and numerical experiments, European Journal of Operational Research, vol.258, issue.1, pp.47-57, 2017.
DOI : 10.1016/j.ejor.2016.10.047

T. Homem-de-mello, L. Vitor, . De-matos, C. Erlon, and . Finardi, Sampling strategies and stopping criteria for stochastic dual dynamic programming: a case study in long-term hydrothermal scheduling, Energy Systems, vol.29, issue.5, pp.1-31, 2011.
DOI : 10.2307/1269769

P. Mahey, J. Koko, and A. Lenoir, Decomposition methods for a spatial model for long-term energy pricing problem, Mathematical Methods of Operations Research, vol.32, issue.1, pp.137-153, 2017.
DOI : 10.1007/BF01586091
URL : https://hal.archives-ouvertes.fr/hal-01691705

V. Mario, . Pereira, M. Leontina, and . Pinto, Multi-stage stochastic optimization applied to energy planning, Mathematical programming, vol.52, issue.1-3, pp.359-375, 1991.

A. Philpott and Z. Guan, On the convergence of stochastic dual dynamic programming and related methods, Operations Research Letters, vol.36, issue.4, pp.450-455, 2008.
DOI : 10.1016/j.orl.2008.01.013

A. Philpott, E. Vitor-de-matos, and . Finardi, On Solving Multistage Stochastic Programs with Coherent Risk Measures, Operations Research, vol.61, issue.4, pp.957-970, 2013.
DOI : 10.1287/opre.2013.1175
URL : http://www.epoc.org.nz/papers/SDDPGeneralRiskv46.pdf

R. Tyrrell and R. , Convex analysis, 1970.

R. Tyrrell, R. Roger, and J. Wets, Stochastic convex programming: relatively complete recourse and induced feasibility, SIAM Journal on Control and Optimization, vol.14, issue.3, pp.574-589, 1976.

A. Shapiro, Analysis of stochastic dual dynamic programming method, European Journal of Operational Research, vol.209, issue.1, pp.63-72, 2011.
DOI : 10.1016/j.ejor.2010.08.007

A. Shapiro, D. Dentcheva, and A. Ruszczy´nskiruszczy´nski, Lectures on stochastic programming: modeling and theory, 2009.
DOI : 10.1137/1.9780898718751
URL : http://epubs.siam.org/doi/pdf/10.1137/1.9780898718751.fm

A. Shapiro, W. Tekaya, P. Joari, . Da-costa, P. Murilo et al., Final report for technical cooperation between georgia institute of technology and ons?operador nacional do sistema elétrico, 2012.

W. Van-ackooij, Y. Welington-de-oliveira, and . Song, On regularization with normal solutions in decomposition methods for multistage stochastic programming. Optimization Online, 2017.