In the field of numerical analysis, the condition number of a function with respect to an argument measures the asymptotically worst case of how much the function can change in proportion to small changes in the argument. The "function" is the solution of a problem and the "arguments" are the data in the problem. A problem with a low condition number is said to be wellconditioned, while a problem with a high condition number is said to be illconditioned. The condition number is a property of the problem. Paired with the problem are any number of algorithms that can be used to solve the problem, that is, to calculate the solution. Some algorithms have a property called backward stability. In general, a backward stable algorithm can be expected to accurately solve wellconditioned problems. Numerical analysis textbooks give formulas for the condition numbers of problems and identify the backward stable algorithms. As a general rule of thumb, if the condition number Îș(A) = 10^{k}, then you lose k digits of accuracy on top of what would be lost to the numerical method due to loss of precision from arithmetic methods.^{[1]}
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Matrices
For example, the condition number associated with the linear equation Ax = b gives a bound on how inaccurate the solution x will be after approximate solution. Note that this is before the effects of roundoff error are taken into account; conditioning is a property of the matrix, not the algorithm or floating point accuracy of the computer used to solve the corresponding system. In particular, one should think of the condition number as being (very roughly) the rate at which the solution, x, will change with respect to a change in b. Thus, if the condition number is large, even a small error in b may cause a large error in x. On the other hand, if the condition number is small then the error in x will not be much bigger than the error in b.
The condition number is defined more precisely to be the maximum ratio of the relative error in x divided by the relative error in b.
Let e be the error in b. Assuming that A is a square matrix, the error in the solution A^{â1}b is A^{â1}e. The ratio of the relative error in the solution to the relative error in b is
This is easily transformed to
The maximum value (for nonzero b and e) is easily seen to be the product of the two operator norms:
The same definition is used for any consistent norm. This number arises so often in numerical linear algebra that it is given a name, the condition number of a matrix.
Of course, this definition depends on the choice of norm.
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