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.
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 ...
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