Abstract
In this paper we develop stochastic simulation methods for solving large systems of linear equations, and focus on two issues:
(1) construction of global random walk algorithms (GRW), in particular, for solving systems of elliptic equations on a grid, and (2) development of local stochastic algorithms based on transforms to balanced
transition matrix. The GRW method
calculates the solution in any desired family of prescribed points of the gird in contrast to the classical stochastic differential equation based Feynman–Kac formula. The use in local random walk methods of balanced transition matrices considerably decreases the variance of the
random estimators and hence decreases the computational cost in comparison with the conventional random walk on grids algorithms.
Jordan canonical forms for systems of elliptic equations
