For small we can use the power-series expansion (30.3.8). Schäfke and Groh (1962) gives corresponding error bounds. If is large we can use the asymptotic expansions in §30.9. Approximations to eigenvalues can be improved by using the continued-fraction equations from §30.3(iii) and §30.8; see Bouwkamp (1947) and Meixner and Schäfke (1954, §3.93).
Another method is as follows. Let be even. For sufficiently large, construct the tridiagonal matrix with nonzero elements
| 30.16.1 | ||||
|
ⓘ
| ||||
and real eigenvalues , , , , arranged in ascending order of magnitude. Then
| 30.16.2 | |||
|
ⓘ
| |||
and
| 30.16.3 | |||
| . | |||
|
ⓘ
| |||
The eigenvalues of can be computed by methods indicated in §§3.2(vi), 3.2(vii). The error satisfies
| 30.16.4 | |||
| . | |||
|
ⓘ
| |||
For , , ,
| 30.16.5 | ||||
which yields . If is odd, then (30.16.1) is replaced by
| 30.16.6 | ||||
|
ⓘ
| ||||
If is large, then we can use the asymptotic expansions referred to in §30.9 to approximate .
If is known, then we can compute (not normalized) by solving the differential equation (30.2.1) numerically with initial conditions , if is even, or , if is odd.
If is known, then can be found by summing (30.8.1). The coefficients are computed as the recessive solution of (30.8.4) (§3.6), and normalized via (30.8.5).
A fourth method, based on the expansion (30.8.1), is as follows. Let be the matrix given by (30.16.1) if is even, or by (30.16.6) if is odd. Form the eigenvector of associated with the eigenvalue , , normalized according to
| 30.16.7 | |||
|
ⓘ
| |||
Then
| 30.16.8 | |||
|
ⓘ
| |||
| 30.16.9 | |||
|
ⓘ
| |||
For error estimates see Volkmer (2004a).
Suggested 2010-05-31 by Lee Van Buren