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The relation between local and global truncation errors is slightly different from in the simpler setting of one-step methods. For linear multistep methods, an additional concept called zero-stability is needed to explain the relation between local and global truncation errors.
Example A: Find the truncation in calculating the first derivative of () = at = using a step size of = ... Solution We have the exact value as ...
To gain insight into the relation of local and global errors, it is helpful to examine simple examples where the exact solution, as well as the approximate solution, can be expressed in explicit formulas. The standard example for this task is the exponential function.
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In the IEEE standard the base is binary, i.e. =, and normalization is used.The IEEE standard stores the sign, exponent, and significand in separate fields of a floating point word, each of which has a fixed width (number of bits).
The name of the method comes from the fact that in the formula above, the function giving the slope of the solution is evaluated at = + / = + +, the midpoint between at which the value of () is known and + at which the value of () needs to be found.
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.
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