Using Randomization to Break the Curse of Dimensionality
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. The author proves that these algorithms succeed in breaking the 'curse of dimensionality' for a subclass of Markovian decision problems known as discrete decision processes.
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Volume (Year): 65 (1997)
Issue (Month): 3 (May)
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- Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
- Ariel Pakes & Paul McGuire, 1997. "Stochastic Algorithms for Dynamic Models: Markov Perfect Equilibrium, and the 'Curse' of Dimensionality," Cowles Foundation Discussion Papers 1144, Cowles Foundation for Research in Economics, Yale University.
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- Lars Peter Hansen & Ellen R. McGrattan & Thomas J. Sargent, 1994.
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