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- Pierre Girardeau, V. Leclere, Andrew B. Philpott
- Math. Oper. Res.
- 2015

We prove the almost-sure convergence of a class of samplingbased nested decomposition algorithms for multistage stochastic convex programs in which the stage costs are general convex functions of the decisions, and uncertainty is modelled by a scenario tree. As special cases, our results imply the almost-sure convergence of SDDP, CUPPS and DOASA when… (More)

- Pierre Carpentier, Jean-Philippe Chancelier, Guy Cohen, Michel De Lara, Pierre Girardeau
- Annals OR
- 2012

For a sequence of dynamic optimization problems, we aim at discussing a notion of consistency over time. This notion can be informally introduced as follows. At the very first time step t0, the decision maker formulates an optimization problem that yields optimal decision rules for all the forthcoming time step t0, t1, . . . , T ; at the next time step t1,… (More)

- Kengy Barty, Pierre Carpentier, Pierre Girardeau
- RAIRO - Operations Research
- 2010

In this paper, we present an Uzawa-based heuristic that is adapted to some type of stochastic optimal control problems. More precisely, we consider dynamical systems that can be divided into small-scale independent subsystems, though linked through a static almost sure coupling constraint at each time step. This type of problem is common in… (More)

- Kengy Barty, Pierre Girardeau, Cyrille Strugarek, Jean-Sébastien Roy
- Monte Carlo Meth. and Appl.
- 2008

We present an algorithm for American option pricing based on stochastic approximation techniques. Option pricing algorithms generally involve some sort of discretization, either on the state space or on the underlying functional space. Our work, which is an application of a more general perturbed gradient algorithm introduced recently by the authors,… (More)

In this paper, we compare the performance of two scenario-based numerical methods to solve stochastic optimal control problems: scenario trees and particles. The problem consists in finding strategies to control a dynamical system perturbed by exogenous noises so as to minimize some expected cost along a discrete and finite time horizon. We introduce the… (More)

In this paper, we present an Uzawa-based heuristic that is adapted to some type of stochastic optimal control problems. More precisely, we consider dynamical systems that can be divided into small-scale independent subsystems, though linked through a static almost sure coupling constraint at each time step. This type of problem is common in… (More)

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