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Using Randomization to Break the Curse of Dimensionality

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Author Info
John Rust
Department of Economics
University of Wisconsin

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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).

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Paper provided by EconWPA in its series Computational Economics with number 9403001.

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Date of creation: 29 Mar 1994
Date of revision: 04 Jul 1994
Handle: RePEc:wpa:wuwpco:9403001

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Web page: http://129.3.20.41

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Find related papers by JEL classification:
C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

References listed on IDEAS
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  1. 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. [Downloadable!] (restricted)
  2. Keane, Michael P & Wolpin, Kenneth I, 1994. "The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 648-72, November. [Downloadable!] (restricted)
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  3. Tauchen, George & Hussey, Robert, 1991. "Quadrature-Based Methods for Obtaining Approximate Solutions to Nonlinear Asset Pricing Models," Econometrica, Econometric Society, vol. 59(2), pages 371-96, March. [Downloadable!] (restricted)
  4. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, December. [Downloadable!]
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This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page.
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