LAPACK routines return four types of floating-point output arguments:
First consider scalars. Let the scalar be an approximation of the true answer . We can measure the difference between and either by the absolute error , or, if is nonzero, by the relative error . Alternatively, it is sometimes more convenient to use instead of the standard expression for relative error (see section 4.2.1). If the relative error of is, say , then we say that is accurate to 5 decimal digits.
In order to measure the error in vectors, we need to measure the size or norm of a vector x . A popular norm is the magnitude of the largest component, , which we denote . This is read the infinity norm of x. See Table 4.2 for a summary of norms.
Table 4.2: Vector and matrix norms
If is an approximation to the exact vector x, we will refer to as the absolute error in (where p is one of the values in Table 4.2), and refer to as the relative error in (assuming ). As with scalars, we will sometimes use for the relative error. As above, if the relative error of is, say , then we say that is accurate to 5 decimal digits. The following example illustrates these ideas:
Thus, we would say that approximates x to 2 decimal digits.
Errors in matrices may also be measured with norms . The most obvious generalization of to matrices would appear to be , but this does not have certain important mathematical properties that make deriving error bounds convenient (see section 4.2.1). Instead, we will use , where A is an m-by-n matrix, or ; see Table 4.2 for other matrix norms. As before is the absolute error in , is the relative error in , and a relative error in of means is accurate to 5 decimal digits. The following example illustrates these ideas:
so is accurate to 1 decimal digit.
Here is some related notation we will use in our error bounds. The condition number of a matrix A is defined as , where A is square and invertible, and p is or one of the other possibilities in Table 4.2. The condition number measures how sensitive is to changes in A; the larger the condition number, the more sensitive is . For example, for the same A as in the last example,
LAPACK error estimation routines typically compute a variable called RCOND , which is the reciprocal of the condition number (or an approximation of the reciprocal). The reciprocal of the condition number is used instead of the condition number itself in order to avoid the possibility of overflow when the condition number is very large. Also, some of our error bounds will use the vector of absolute values of x, (), or similarly ().
Now we consider errors in subspaces. Subspaces are the outputs of routines that compute eigenvectors and invariant subspaces of matrices. We need a careful definition of error in these cases for the following reason. The nonzero vector x is called a (right) eigenvector of the matrix A with eigenvalue if . From this definition, we see that -x, 2x, or any other nonzero multiple of x is also an eigenvector. In other words, eigenvectors are not unique. This means we cannot measure the difference between two supposed eigenvectors and x by computing , because this may be large while is small or even zero for some . This is true even if we normalize n so that , since both x and -x can be normalized simultaneously. So in order to define error in a useful way, we need to instead consider the set S of all scalar multiples of x. The set S is called the subspace spanned by x, and is uniquely determined by any nonzero member of S. We will measure the difference between two such sets by the acute angle between them. Suppose is spanned by and S is spanned by {x}. Then the acute angle between and S is defined as
One can show that does not change when either or x is multiplied by any nonzero scalar. For example, if
as above, then for any nonzero scalars and .
Here is another way to interpret the angle between and S. Suppose is a unit vector (). Then there is a scalar such that
The approximation holds when is much less than 1 (less than .1 will do nicely). If is an approximate eigenvector with error bound , where x is a true eigenvector, there is another true eigenvector satisfying . For example, if
then for .
Some LAPACK routines also return subspaces spanned by more than one vector, such as the invariant subspaces of matrices returned by xGEESX. The notion of angle between subspaces also applies here; see section 4.2.1 for details.
Finally, many of our error bounds will contain a factor p(n) (or p(m , n)), which grows as a function of matrix dimension n (or dimensions m and n). It represents a potentially different function for each problem. In practice, the true errors usually grow just linearly; using p(n) = 10n in the error bound formulas will often give a reasonable bound. Therefore, we will refer to p(n) as a ``modestly growing'' function of n. However it can occasionally be much larger, see section 4.2.1. For simplicity, the error bounds computed by the code fragments in the following sections will use p(n) = 1. This means these computed error bounds may occasionally slightly underestimate the true error. For this reason we refer to these computed error bounds as ``approximate error bounds''.