Transcription of NUMERICAL STABILITY; IMPLICIT METHODS
{{id}} {{{paragraph}}}
Example: bankruptcy
NUMERICAL STABILITY; IMPLICIT METHODS
NUMERICAL stability ; IMPLICIT METHODS . When solving the initial value problem Y 0 (x) = f (x, Y (x)), x0 x b Y (x0 ) = Y0. we know that small changes in the initial data Y0 will result in small changes in the solution of the differential equation. More precisely, consider the perturbed problem Y 0 (x) = f (x, Y (x)), x0 x b Y (x0 ) = Y0 + . Then assuming f (x, z) and f (x, z)/ z are continuous for x0 x b, < z < , we have max |Y (x) Y (x)| c | |. x0 x b for some constant c > 0. We would like our NUMERICAL METHODS to have a similar property. Consider the Euler method yn+1 = yn + hf (xn , yn ) , n = 0, 1, .. y0 = Y0. and then consider the perturbed problem . yn+1 = yn + hf (xn , yn ) , n = 0, 1, .. y0 = Y0 + . We can show the following: max |yn yn | cb | |. x0 xn b for some constant cb > 0 and for all sufficiently small values of the stepsize h. This implies that Euler's method is stable, and in the same manner as was true for the original differential equation problem. The general idea of stability for a NUMERICAL method is essentially that given above for Eulers's method.
THE TRAPEZOIDAL METHOD The backward Euler method is stable, but still is lacking in accuracy. A similar but more accurate numerical method is the trapezoidal method: y n+1 = y n + h 2 [f (x n;y n) + f (x n+1;y n+1)]; n = 0;1;::: (6) It is derived by applying the simple trapezoidal numerical integration rule to the equation Y(x n+1) = Y(x n) + Z ...
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: