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The application of MacCormack method to the above equation proceeds in two steps; a predictor step which is followed by a corrector step. Predictor step: In the predictor step, a "provisional" value of u {\displaystyle u} at time level n + 1 {\displaystyle n+1} (denoted by u i p {\displaystyle u_{i}^{p}} ) is estimated as follows
This algorithm can easily be adapted to compute the variance of a finite population: simply divide by n instead of n − 1 on the last line.. Because SumSq and (Sum×Sum)/n can be very similar numbers, cancellation can lead to the precision of the result to be much less than the inherent precision of the floating-point arithmetic used to perform the computation.
The only thing constant is change. This is certainly more true of software systems than almost any phenomenon, [6] not all software change in the same way so software fault tolerance methods are designed to overcome execution errors by modifying variable values to create an acceptable program state. [7]
In an analogous way, one can obtain finite difference approximations to higher order derivatives and differential operators. For example, by using the above central difference formula for f ′(x + h / 2 ) and f ′(x − h / 2 ) and applying a central difference formula for the derivative of f ′ at x, we obtain the central difference approximation of the second derivative of f:
Note the formula on the dot-matrix line above and the answer on the seven-segment line below, as well as the arrow keys allowing the entry to be reviewed and edited. This calculator program has accepted input in infix notation, and returned the answer , ¯. Here the comma is a decimal separator.
There is a difference between fault tolerance and systems that rarely have problems. For instance, the Western Electric crossbar systems had failure rates of two hours per forty years, and therefore were highly fault resistant. But when a fault did occur they still stopped operating completely, and therefore were not fault tolerant.
The divided difference formulas are more versatile, useful in more kinds of problems. The Lagrange formula is at its best when all the interpolation will be done at one x value, with only the data points' y values varying from one problem to another, and when it is known, from past experience, how many terms are needed for sufficient accuracy.
Simpler to program, requires less computer time per step, and works well with multigrid acceleration techniques; Has a free parameter in conjunction with the fourth-difference dissipation, which is needed to approach a steady state. More accurate than the first-order upwind scheme if the Peclet number is less than 2. [3]