Low-discrepancy sampling for approximate dynamic programming with local approximators

DOI10.1016/j.cor.2013.09.006zbMath1348.90616OpenAlexW2097394825WikidataQ59403922 ScholiaQ59403922MaRDI QIDQ336896

Cristiano Cervellera, Mauro Gaggero, Danilo Macciò

Publication date: 10 November 2016

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cor.2013.09.006

zbMATH Keywords

approximate dynamic programminglocal approximationinventory forecastinglow-discrepancy samplingNadaraya-Watson models

Mathematics Subject Classification ID

Inventory, storage, reservoirs (90B05) Dynamic programming (90C39) Markov and semi-Markov decision processes (90C40)

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Efficient approximate dynamic programming based on design and analysis of computer experiments for infinite-horizon optimization ⋮ Approximate stochastic dynamic programming for hydroelectric production planning ⋮ Approximate dynamic programming for stochastic \(N\)-stage optimization with application to optimal consumption under uncertainty

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