Abstract

A method for fast and efficient numerical solution of stochastic dynamic programming problems is described. The problems arise in optimal control of nonlinear, continuous-time dynamical systems, perturbed by Poisson as well as Gaussian random white noise. The numerical formulation is highly suitable for a vector multiprocessor or vectorizing supercomputer. Advanced computing techniques and hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations. >

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