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

For
,
,
,
which yields
. If
is odd, then (30.16.1) is replaced by
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
Then
For error estimates see Volkmer (2004a).
Suggested 2010-05-31 by Lee Van Buren