Higher Order Aitken Extrapolation with Application to Converging and Diverging Gauss-Seidel Iterations

Abstract

Aitken extrapolation normally applied to convergent fixed point iteration is extended to extrapolate the solution of a divergent iteration. In addition, higher order Aitken extrapolation is introduced that enables successive decomposition of high Eigen values of the iteration matrix to enable convergence. While extrapolation of a convergent fixed point iteration using a geometric series sum is a known form of Aitken acceleration, it is shown in this paper that the same formula can be used to estimate the solution of sets of linear equations from diverging Gauss Seidel iterations. In both convergent and divergent iterations, the ratios of differences among the consecutive values of iteration eventually form a convergent or divergent series with a factor equal to the largest Eigen value of the iteration matrix. Higher order Aitken extrapolation is shown to eliminate the influence of dominant Eigen values of the iteration matrix in successive order until the iteration is determined by the lowest possible Eigen value. For the convergent part of the Gauss Seidel iteration, further acceleration is made possible by coupling of the extrapolation technique with the successive over relaxation method. Application examples from both convergent and divergent iterations have been provided. Coupling of the extrapolation with the successive over relaxation technique is also illustrated for a steady state two dimensional heat flow problem which was solved using MATLAB programming.