Dual SDDP for risk-averse multistage stochastic programs

Bernardo Freitas Paulo da Costa; Vincent Leclère

doi:10.1016/j.orl.2023.04.001

Dual SDDP for risk-averse multistage stochastic programs

Bernardo Freitas Paulo da Costa
, Vincent Leclère

Getulio Vargas Foundation (FGV)

Research output: Contribution to journal › Article › peer-review

Abstract

Risk-averse multistage stochastic programs appear in multiple areas and are challenging to solve. Stochastic Dual Dynamic Programming (SDDP) is a well-known tool to address such problems under time-independence assumptions. We show how to derive a dual formulation for these problems and apply an SDDP algorithm, leading to converging and deterministic upper bounds for risk-averse problems.

Original language	English
Pages (from-to)	332-337
Number of pages	6
Journal	Operations Research Letters
Volume	51
Issue number	3
DOIs	https://doi.org/10.1016/j.orl.2023.04.001
Publication status	Published - 1 May 2023

Keywords

Duality
Dynamic programming
Risk measures
SDDP
Stochastic programming

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10.1016/j.orl.2023.04.001

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title = "Dual SDDP for risk-averse multistage stochastic programs",

abstract = "Risk-averse multistage stochastic programs appear in multiple areas and are challenging to solve. Stochastic Dual Dynamic Programming (SDDP) is a well-known tool to address such problems under time-independence assumptions. We show how to derive a dual formulation for these problems and apply an SDDP algorithm, leading to converging and deterministic upper bounds for risk-averse problems.",

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AU - Leclère, Vincent

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N2 - Risk-averse multistage stochastic programs appear in multiple areas and are challenging to solve. Stochastic Dual Dynamic Programming (SDDP) is a well-known tool to address such problems under time-independence assumptions. We show how to derive a dual formulation for these problems and apply an SDDP algorithm, leading to converging and deterministic upper bounds for risk-averse problems.

AB - Risk-averse multistage stochastic programs appear in multiple areas and are challenging to solve. Stochastic Dual Dynamic Programming (SDDP) is a well-known tool to address such problems under time-independence assumptions. We show how to derive a dual formulation for these problems and apply an SDDP algorithm, leading to converging and deterministic upper bounds for risk-averse problems.

KW - Duality

KW - Dynamic programming

KW - Risk measures

KW - SDDP

KW - Stochastic programming

UR - https://www.scopus.com/pages/publications/85153685070

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DO - 10.1016/j.orl.2023.04.001

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VL - 51

SP - 332

EP - 337

JO - Operations Research Letters

JF - Operations Research Letters

IS - 3

ER -

Dual SDDP for risk-averse multistage stochastic programs

Abstract

Keywords

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