J. References-alais, Risque et optimisation pour le management d'´ energies, 2013.

L. Bacaud, C. Lemaréchal, A. Renaud, and C. A. Sagastizábal, Bundle methods in stochastic optimal power management: A disaggregated approach using preconditioner, Computational Optimization and Applications, vol.20, pp.227-244, 2001.

K. Barty, P. Carpentier, and P. Girardeau, Decomposition of largescale stochastic optimal control problems, RAIRO Operations Research, vol.44, pp.167-183, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00362135

K. Barty, J. Roy, and C. Strugarek, A stochastic gradient type algorithm for closed-loop problems, Mathematical Programming, Series A, vol.119, pp.51-78, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00983337

R. Bellman, Dynamic Programming, 1957.

D. P. Bertsekas, Dynamic Programming and Optimal Control: Approximate Dynamic Programming, 2012.

D. P. Bertsekas and J. N. Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, 1996.

P. Carpentier, J. Chancelier, G. Cohen, and M. Lara, Stochastic Multi-Stage Optimization volume 75 of Probability Theory and Stochastic Modelling, 2015.

P. Carpentier, C. Cohen, J. Culioli, and A. Renaud, Stochastic optimization of unit commitment: a new decomposition framework, IEEE Transactions on Power Systems, vol.11, pp.1067-1073, 1996.

G. Cohen, Optimization by Decomposition and Coordination: A Unified Approach, IEEE Transactions on Automatic Control, vol.23, pp.222-232, 1978.

G. Cohen, Auxiliary Problem Principle and decomposition of optimization problems, Journal of Optimization Theory and Applications, vol.32, pp.277-305, 1980.

G. Cohen and J. Culioli, Decomposition Coordination Algorithms for Stochastic Optimization, SIAM Journal on Control and Optimization, vol.28, pp.1372-1403, 1990.

C. Dellacherie and P. A. Meyer, Probabilités et potentiel. Hermann, 1975.

I. Dunning, J. Huchette, and M. Lubin, JuMP: A modeling language for mathematical optimization, SIAM Review, vol.59, pp.295-320, 2017.

I. Ekeland and R. Temam, Convex Analysis and Variational Problems, Classics in Applied Mathematics. SIAM, vol.28, 1999.

J. C. Gilbert and X. Jonsson, Libopt-an environment for testing solvers on heterogeneous collections of problems, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00135013

P. Girardeau, Résolution de grandsprobì emes en optimisation stochastique dynamique, 2010.

P. Girardeau, V. Leclere, and A. B. Philpott, On the convergence of decomposition methods for multistage stochastic convex programs, Mathematics of Operations Research, vol.40, pp.130-145, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01208295

V. Guigues, Dual dynamic programing with cut selection: Convergence proof and numerical experiments, European Journal of Operational Research, vol.258, pp.47-57, 2017.

H. Heitsch and W. Römisch, Scenario tree modelling for multistage stochastic programs, Mathematical Programming, vol.118, pp.371-406, 2009.

H. Heitsch, W. Römisch, and C. Strugarek, Stability of multistage stochastic programs, SIAM Journal on Optimization, vol.17, pp.511-525, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00977507

D. Kuhn, W. Wiesemann, and A. Georghiou, Primal and dual linear decision rules in stochastic and robust optimization, Mathematical Programming, vol.130, pp.177-209, 2011.

V. Lecì-ere, Contributions aux méthodes de décomposition en optimisation stochastique, 2014.

A. Lenoir and P. Mahey, A survey on operator splitting and decomposition of convex programs, RAIRO Operations Research, vol.51, pp.17-41, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01691690

M. E. Maceira and J. M. Damazio, Use of par(p) model in the stochastic dual dynamic programming optimization scheme used used in the operation planning of the brazilian hydropower system, Probability in the Engineering and Informational Sciences, vol.20, pp.143-156, 2006.

V. L. De-matos, A. B. Philpott, and E. C. Finardi, Improving the performance of stochastic dual dynamic programming, Journal of Computational and Applied Mathematics, vol.290, pp.196-208, 2015.

M. V. Pereira and L. M. Pinto, Multi-stage stochastic optimization applied to energy planning, Mathematical Programming, vol.52, pp.359-375, 1991.
DOI : 10.1007/bf01582895

G. C. Pflug, A. Pichler, A. B. Philpott, and Z. Guan, On the convergence of stochastic dual dynamic programming and related methods, Operations Reseach Letters, vol.36, pp.450-455, 2008.

W. B. Powell, Approximate Dynamic Programming: Solving the Curses of Dimensionality, vol.842, 2011.

M. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, 1994.

R. T. Rockafellar, Integrals which are convex functionals, Pacific Journal of Mathematics, vol.24, pp.525-539, 1968.
DOI : 10.2140/pjm.1968.24.525
URL : http://msp.org/pjm/1968/24-3/pjm-v24-n3-p11-s.pdf

R. T. Rockafellar, Integrals which are convex functionals, ii, Pacific Journal of Mathematics, vol.39, pp.439-469, 1971.
DOI : 10.2140/pjm.1971.39.439
URL : http://msp.org/pjm/1971/39-2/pjm-v39-n2-p10-s.pdf

R. T. Rockafellar, R. J. Wets, and .. , Scenarios and policy aggregation in optimization under uncertainty, Math. Oper. Res, vol.16, pp.119-147, 1991.
DOI : 10.1287/moor.16.1.119
URL : http://pure.iiasa.ac.at/id/eprint/2933/1/WP-87-119.pdf

A. Ruszczy´nskiruszczy´nski, Decomposition methods in stochastic programming, Mathematical programming, vol.79, pp.333-353, 1997.

, Stochastic Programming volume 10 of Handbooks in Operations Research and Management Science, 2003.

A. Shapiro, On complexity of multistage stochastic programs. Operations Research Letters, vol.34, pp.1-8, 2006.

A. Shapiro, Analysis of Stochastic Dual Dynamic Programming Method, European Journal of Operational Research, vol.209, pp.63-72, 2011.

A. Shapiro, D. Dentcheva, and A. Ruszczy´nskiruszczy´nski, Lectures on Stochastic Programming. Society for Industrial and Applied Mathematics, 2009.

C. Strugarek, Approches variationnelles et autres contributions en optimisation stochastique, 2006.
URL : https://hal.archives-ouvertes.fr/pastel-00001848

S. Takriti, J. R. Birge, and E. Long, A stochastic model for the unit commitment problem, IEEE Transactions on Power Systems, vol.11, pp.1497-1508, 1996.

J. N. Tsitsiklis and B. Van-roy, Feature-based methods for large-scale dynamic programming, Machine Learning, vol.22, pp.59-94, 1996.

R. M. Van-slyke and R. Wets, L-shaped linear programs with applications to optimal control and stochastic programming, SIAM Journal on Applied Mathematics, vol.17, pp.638-663, 1969.

P. Vezolle, S. Vialle, and X. Warin, Large Scale Experiment and Optimization of a Distributed Stochastic Control Algorithm. Application to Energy Management Problems, International workshop on Large-Scale Parallel Processing, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00390290

J. Zou, S. Ahmed, and X. A. Sun, Stochastic dual dynamic integer programming, pp.1-42, 2017.