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Suppose we have a continuous differential equation ′ = (,), =, and we wish to compute an approximation of the true solution () at discrete time steps ,, …,.For simplicity, assume the time steps are equally spaced:
Explicit examples from the linear multistep family include the Adams–Bashforth methods, and any Runge–Kutta method with a lower diagonal Butcher tableau is explicit. A loose rule of thumb dictates that stiff differential equations require the use of implicit schemes, whereas non-stiff problems can be solved more efficiently with explicit ...
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Composite Simpson's 3/8 rule is even less accurate. Integration by Simpson's 1/3 rule can be represented as a weighted average with 2/3 of the value coming from integration by the trapezoidal rule with step h and 1/3 of the value coming from integration by the rectangle rule with step 2h. The accuracy is governed by the second (2h step) term.
Is it worth showing how to find the local truncation errors of, say, Euler method and the classical Runge–Kutta_methods? I'm reluctant to do so because such an analysis is already covered in Euler method. I thought it would have been instructional to have such an explanation next to their definitions. Maybe a link to Euler method will suffice.
In cases where the integration is permitted to extend over equidistant sections of the interval [,], the composite Boole's rule might be applied. Given N {\displaystyle N} divisions, where N {\displaystyle N} mod 4 = 0 {\displaystyle 4=0} , the integrated value amounts to: [ 4 ]
Time series of the Tent map for the parameter m=2.0 which shows numerical error: "the plot of time series (plot of x variable with respect to number of iterations) stops fluctuating and no values are observed after n=50". Parameter m= 2.0, initial point is random.
Heun's Method addresses this problem by considering the interval spanned by the tangent line segment as a whole. Taking a concave-up example, the left tangent prediction line underestimates the slope of the curve for the entire width of the interval from the current point to the next predicted point.