Browsing by Author "Hussein, Esam, M., A."

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    A comparison of vectorizable discrete sampling methods in Monte Carlo applications
    (1995) Sarno, Riyanarto; Bhavsar, Virendra, C.; Hussein, Esam, M., A.
    The performance of various vectorizable discrete random-sampling methods, along with the commonly used inverse sampling method, is assessed on a vector machine. Monte Carlo applications involving, one-dimensional, two-dimensional and multi-dimensional probability tables are used in the investigation. Various forms of the weighted sampling method and methods that transform the original probability table are examined. It is found that some form of weighted sampling is efficient, when the original probability distribution is not far from uniform or can be approximated analytically. Table transformation methods, though require additional memory storage, are best suited in applications where multi-dimensional tables are involved. Keywords: Discrete sampling, Weighted sampling, Monte Carlo simulations, Vector processing.
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    Vectorized Monte Carlo solutions of linear equations
    (1992) Sarno, Rlyanarto; Bhavsar, Vlrendra, C.; Hussein, Esam, M., A.
    Monte Carlo (MC) methods for solving a system of linear equations exhibit high parallelism. The vectorization and incorporation of various sampling methods into the MC methods are investigated to speed up the computations. The weighted sampling method and an alias-like method are found to reduce the order of the time complexity of the MC methods from n[superscript 3], when using the inverse method, to n[superscript 2] for estimating n unknowns. This results into faster solutions with scalar as well as vector processing as demonstrated with computational studies on an IBM 3090-180 computer with a vector facility. The MC methods are shown to be attractive when the number of unknowns is very large and the estimation of only a very small number of unknowns is required.