The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Using Randomization to Break the Curse of Dimensionality
Abstract
Abstract: This paper introduces random versions of successive approximations and multigrid algorithms for computing approximate solutions to a class of finite and infinite horizon Markovian decision problems (MDPs). We prove that these algorithms succeed in breaking the “curse of dimensionality ” for a subclass of MDPs known as discrete decision processes (DDPs). 1
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Cites
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Cited by
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Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
Algorithms for Reinforcement Learning
Szepesvári, C. (2010) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.1007/978-3-031-01551-9 · Cited by 1,049
Monte Carlo methods for security pricing
Boyle, P., Broadie, M., & Glasserman, P. (1997) · Journal of Economic Dynamics and Control · doi:10.1016/s0165-1889(97)00028-6 · Cited by 854
A survey of computational complexity results in systems and control
Blondel, V. D., & Tsitsiklis, J. N. (2000) · Automatica · doi:10.1016/s0005-1098(00)00050-9 · Cited by 732
Metadata
- Authors
- Rust, J.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1997-05
- Volume
- 65
- Issue
- 3
- Pages
- 487
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W1563483395
- Cited by
- 407
- References
- 1
- FWCI
- 11.65
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Using Randomization to Break the Curse of Dimensionality. https://app.cochat.ai/papers/10.2307/2171751
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- Rust, J. (1997). Using Randomization to Break the Curse of Dimensionality. Econometrica. https://doi.org/10.2307/2171751
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
