Hybrid Methods for Continuous Space Dynamic Programming
AbstractWe propose a method for solving continuous-state and action-stochastic dynamic programs that is a hybrid between the continuous space projection methods introduced by Judd and the discrete space methods introduced by Bellman. Our hybrid approach yields a smooth representation of the value function while preserving the computational simplicity of discrete dynamic programming. Our method is especially well suited for implementation in a vector processing environment such as MATLAB or GAUSS, and makes it possible to automate the setup and solution of continuous space dynamic programs in a way that previously seemed elusive.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 1332.
Date of creation: 01 Mar 1999
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