Efficient approximate dynamic programming based on design and analysis of computer experiments for infinite-horizon optimization
Ying Chen, Feng Liu, Jay M. Rosenberger, Victoria C.P. Chen, Asama Kulvanitchaiyanunt, Yuan Zhou
| Year of publication: |
2020
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|---|---|
| Authors: | Chen, Ying ; Liu, Feng ; Rosenberger, Jay M. ; Chen, Victoria C. P. ; Kulvanitchaiyanunt, Asama ; Zhou, Yuan |
| Published in: |
Computers & operations research : and their applications to problems of world concern ; an international journal. - Oxford [u.a.] : Elsevier, ISSN 0305-0548, ZDB-ID 194012-0. - Vol. 124.2020, p. 1-14
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| Subject: | Approximate dynamic programming | Extrapolation | State space sampling | Stopping criterion | Theorie | Theory | Dynamische Optimierung | Dynamic programming | Mathematische Optimierung | Mathematical programming | Stichprobenerhebung | Sampling |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1016/j.cor.2020.105032 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012300890