Randomized linear programming solves the Markov decision problem in nearly linear (sometimes sublinear) time
Mengdi Wang
| Year of publication: |
2020
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|---|---|
| Authors: | Wang, Mengdi |
| Published in: |
Mathematics of operations research. - Catonsville, MD : INFORMS, ISSN 0364-765X, ZDB-ID 195683-8. - Vol. 45.2020, 2, p. 517-546
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| Subject: | Markov decision process | randomized algorithm | linear programming | duality | primal-dual method | runtime complexity | stochastic approximation | Mathematische Optimierung | Mathematical programming | Theorie | Theory | Markov-Kette | Markov chain | Entscheidung | Decision | Algorithmus | Algorithm | Stochastischer Prozess | Stochastic process |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1287/moor.2019.1000 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012242520