Comparison of Sampling Methods for Dynamic Stochastic Programming

DOI10.1007/978-1-4419-9586-5_16zbMath1405.90087OpenAlexW2136792742MaRDI QIDQ4613830

E. A. Medova, Yee Sook Yong, Michael A. H. Dempster

Publication date: 25 January 2019

Published in: International Series in Operations Research & Management Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-1-4419-9586-5_16

zbMATH Keywords

stochastic programmingdiscretization errorscenario generationsampling methodsin-sample and out-of-sample testsscenario-based approximation

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Stochastic programming (90C15) Dynamic programming (90C39)

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Cites Work

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• Generating Scenario Trees for Multistage Decision Problems
• No-arbitrage conditions, scenario trees, and multi-asset financial optimization
• A heuristic for moment-matching scenario generation
• Financial scenario generation for stochastic multi-stage decision processes as facility location problems
• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
• Monte Carlo sampling approach to stochastic programming
• Scenarios for multistage stochastic programs

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