Structure of risk-averse multistage stochastic programs

Dupačová, Jitka; Kozmík, Václav

doi:10.1007/s00291-014-0379-2

Cited by 19 publications

(14 citation statements)

References 32 publications

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“…The convergence analysis of SDDP was revisited in [45], and more recently in [27]. Recent publications have addressed the issue of incorporating risk-averse measures into the SDDP algorithm: [3,22,27,[46][47][48][49]. Polyhedral risk measures were studied in [50] and [46].…”

Section: Introductionmentioning

confidence: 99%

On conditional cuts for stochastic dual dynamic programming

Ackooij¹,

Warin²

2020

EURO Journal on Computational Optimization

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Multi stage stochastic programs arise in many applications from engineering whenever a set of inventories or stocks has to be valued. Such is the case in seasonal storage valuation of a set of cascaded reservoir chains in hydro management. A popular method is Stochastic Dual Dynamic Programming (SDDP), especially when the dimensionality of the problem is large and Dynamic programming no longer an option. The usual assumption of SDDP is that uncertainty is stage-wise independent, which is highly restrictive from a practical viewpoint. When possible, the usual remedy is to increase the statespace to account for some degree of dependency. In applications this may not be possible or it may increase the state space by too much. In this paper we present an alternative based on keeping a functional dependency in the SDDP -cuts related to the conditional expectations in the dynamic programming equations. Our method is based on popular methodology in mathematical finance, where it has progressively replaced scenario trees due to superior numerical performance. On a set of numerical examples, we too show the interest of this way of handling dependency in uncertainty, when combined with SDDP. Our method is readily available in the open source software package StOpt.

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Section: Introductionmentioning

confidence: 99%

On conditional cuts for stochastic dual dynamic programming

Ackooij¹,

Warin²

2020

EURO Journal on Computational Optimization

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“…-Discontinuous stochastic dynamics in the market, Choquet uncertainty and Knightian ambiguity underlie and characterize the modeling framework in articles Davis and Lleo (2015), Desmettre et al (2015), Driouchi et al (2015), and Pagliarani and Vargiolu (2015). -Stochastic dominance and dynamic time consistency of decision processes within optimization problems are key concepts in articles Konicz et al (2015), Thiele and Cetinkaya (2015), Dupacova and Kozmik (2015), and Kopa and Post (2015). -Article Dupacova and Kozmik (2015) has a strong dynamic stochastic programming flavor, while article Konicz et al (2015) combines a stochastic programming with a stochastic control modeling framework.…”

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confidence: 99%

“…-Stochastic dominance and dynamic time consistency of decision processes within optimization problems are key concepts in articles Konicz et al (2015), Thiele and Cetinkaya (2015), Dupacova and Kozmik (2015), and Kopa and Post (2015). -Article Dupacova and Kozmik (2015) has a strong dynamic stochastic programming flavor, while article Konicz et al (2015) combines a stochastic programming with a stochastic control modeling framework. The latter is a key ingredient to the comprehension of articles Davis and Lleo (2015), Desmettre et al (2015) and Pagliarani and Vargiolu (2015).…”

mentioning

confidence: 99%

“…-Articles Dupacova and Kozmik (2015), Konicz et al (2015), Davis and Lleo (2015), Desmettre et al (2015) and Pagliarani and Vargiolu (2015) focus, under alternative modeling approaches, on dynamic portfolio selection problems, leading to closed-form solutions or qualified characterizations of optimal policies. -Articles Bruni et al (2015) and Thiele and Cetinkaya (2015) address static portfolio selection problems within a rather general set of assumption on market returns and decision criteria.…”

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confidence: 99%

“…-Discrete-time optimization and modeling frameworks characterize the contributions Bruni et al (2015); Kopa and Post (2015); Dupacova and Kozmik (2015); Konicz et al (2015): here, decision processes are defined in discrete time despite an underlying continuous time evolution, and the statistical characterization leads to highly realistic problem formulations, due to a positive trade-off between formulation realism and numerical complexity. -Discontinuous stochastic dynamics in the market, Choquet uncertainty and Knightian ambiguity underlie and characterize the modeling framework in articles Davis and Lleo (2015), Desmettre et al (2015), Driouchi et al (2015), and Pagliarani and Vargiolu (2015).…”

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confidence: 99%

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