Tolerance¶

In engineering and science, error is a deviation from an expected or computed value. Tolerance is the level of error that is acceptable for an engineering application. We say that a computer program has converged to a solution when it has found a solution with an error smaller than the tolerance. When computing roots numerically, or conducting any other kind of numerical analysis, it is important to establish both a metric for error and a tolerance that is suitable for a given engineering/science application.

For computing roots, we want an \(x_r\) such that \(f(x_r)\) is very close to 0. Therefore \(|f(x)|\) is a possible choice for the measure of error since the smaller it is, the likelier we are to a root. Also if we assume that \(x_i\) is the \(i\)th guess of an algorithm for finding a root, then \(|x_{i+1} - x_i|\) is another possible choice for measuring error, since we expect the improvements between subsequent guesses to diminish as it approaches a solution. As will be demonstrated in the following examples, these different choices have their advantages and disadvantages.

TRY IT! Let error be measured by \(e = |f(x)|\) and tol be the acceptable level of error. The function \(f(x) = x^2 + \text{tol}/2\) has no real roots. However, \(|f(0)| = {\text{tol}}/2\) and is therefore acceptable as a solution for a root finding program.

TRY IT! Let error be measured by \(e = |x_{i+1} - x_i|\) and tol be the acceptable level of error. The function \(f(x) = 1/x\) has no real roots, but the guesses \(x_i = -{\text{tol}}/4\) and \(x_{i+1} = {\text{tol}}/4\) have an error of \(e = {\text{tol}}/2\) and is an acceptable solution for a computer program.

Based on these observations, the use of tolerance and converging criteria must be done very carefully and in the context of the program that uses them.

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Python Numerical Methods

Tolerance¶