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 (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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Year of publication:	1994-03-29
Authors:	Rust, John
Institutions:	EconWPA ; Department of Economics

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Extent:	application/pdf application/postscript
Series:	Computational Economics.
Type of publication:	Book / Working Paper
Notes:	TeX file, Postscript version submitted
Classification:	C8 - Data Collection and Data Estimation Methodology; Computer Programs
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005134592