Monte Carlo Algorithm Almost Optimal Density Function
Issue Date:
2000
Publisher:
Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation:
Pliska Studia Mathematica Bulgarica, Vol. 13, No 1, (2000), 117p-132p
Abstract:
An iterative Monte Carlo algorithm for evaluating linear functionals of the solution
of integral equations with polynomial non-linearity is proposed and studied. The
method uses a simulation of branching stochastic processes. It is proved that the
mathematical expectation of the introduced random variable is equal to a linear
functional of the solution. The algorithm uses the so-called almost optimal density
function. Numerical examples are considered. Parallel implementation of the algorithm
is also realized using the package ATHAPASCAN as an environment for parallel
realization.The computational results demonstrate high parallel efficiency of the
presented algorithm and give a good solution when almost optimal density function is used as a transition density.
