Differentiability and Regularization of Parametric Convex Value Functions in Stochastic Multistage Optimization - Archive ouverte HAL Access content directly
Reports Year : 2023

Differentiability and Regularization of Parametric Convex Value Functions in Stochastic Multistage Optimization

Abstract

In multistage decision problems, it is often the case that an initial strategic decision (such as investment) is followed by many operational ones (operating the investment). Such initial strategic decision can be seen as a parameter affecting a multistage decision problem. More generally, we study in this paper a standard multistage stochastic optimization problem depending on a parameter. When the parameter is fixed, Stochastic Dynamic Programming provides a way to compute the optimal value of the problem. Thus, the value function depends both on the state (as usual) and on the parameter. Our aim is to investigate on the possibility to efficiently compute gradients of the value function with respect to the parameter, when these objects exist. When nondifferentiable, we propose a regularization method based on the Moreau-Yosida envelope. We present a numerical test case from day-ahead power scheduling.
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Dates and versions

hal-03903218 , version 1 (16-12-2022)
hal-03903218 , version 2 (11-01-2023)

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Adrien Le Franc, Pierre Carpentier, Jean-Philippe Chancelier, Michel de Lara. Differentiability and Regularization of Parametric Convex Value Functions in Stochastic Multistage Optimization. ENPC - École des Ponts ParisTech. 2023. ⟨hal-03903218v2⟩
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