Optimization techniques for stochastic dynamic programming

Supercomputer optimizations for a computational method of solving stochastic dynamic programming problems are presented. The computational method is valid for a general class of optimal control problems that are nonlinear dynamic systems perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian, as well as jump Poisson, noise. Optimization techniques for vector multiprocessors or vectorizing supercomputers include advanced data structures, loop restructuring, and compiler directives. These advanced computing techniques and supercomputing hardware help alleviate Bellman's 'curse of dimensionality' in dynamic programming computations, by permitting the solution of larger problems.<
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