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
and real eigenvalues
,
,
,
,
arranged in ascending order of magnitude. Then
and
The eigenvalues of
can be computed by methods indicated in
§§3.2(vi), 3.2(vii). The error satisfies
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.