Comparison of Monte Carlo Algorithms for Obtaining Geometric Convergence for Model Transport Problems
Two quite different methods for accelerating the convergence of global Monte Carlo solutions of continuous transport problems have been developed recently in Claremont. One of these is based on a sequential form of correlated sampling, first proposed for matrix problems by Halton[l]. The second method makes use of importance sampling transformations applied adaptively. These two methods are contrasted and compared for a family of model transport problems in one dimension.
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