| Abstrakt: |
This paper concerns the stability analysis of numerical methods for solving time dependent ordinary and partial differential equations. In the literature stability estimates for such methods were derived, under a condition which can be viewed as a transplantation of the Kreiss resolvent condition (from the unit disk to the stability region S of the numerical method). These estimates tell us that errors in the numerical time stepping process cannot grow faster than linearly with min {s,n}. Here n denotes the number of time steps, and s stands for the order of the (spatial discretization) matrices involved.In this paper we address the natural question of whether the above stability estimates can be improved so as to imply an error growth at a slower rate than min {s,n} (when n→∞, s→∞). Our results concerning this question are as follows: (a) for all (practical) RungeKutta and other one-step formulas, we show that the estimates from the literature are sharp in that error growth at the rate min {s,n} can actually occur, (b) for linear multistep formulas we find that, rather surprisingly, some of the stability estimates can substantially be improved and extended, whereas others are sharp.The results proved in this paper are also relevant to (suitably scaled spatial discretization) matrices whose ε-pseudo-eigenvalues lie at a distance not exceeding Kε from the stability region S of the time stepping method, for all ε>0 and fixed constant K. [ABSTRACT FROM AUTHOR] |
Full text from SpringerLink