30.15 Signal Analysis30.17 Tables

§30.16 Methods of Computation

Contents

§30.16(i) Eigenvalues

For small |\gamma^{2}| we can use the power-series expansion (30.3.8). Schäfke and Groh (1962) gives corresponding error bounds. If |\gamma^{2}| 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 n-m be even. For d sufficiently large, construct the d\times d tridiagonal matrix \mathbf{A}=[A_{{j,k}}] with nonzero elements

30.16.1
A_{{j,j}}=(m+2j-2)(m+2j-1)-2\gamma^{2}\frac{(m+2j-2)(m+2j-1)-1+m^{2}}{(2m+4j-5)(2m+4j-1)},
A_{{j,j+1}}=-\gamma^{2}\frac{(2m+2j-1)(2m+2j)}{(2m+4j-1)(2m+4j+1)},
A_{{j,j-1}}=-\gamma^{2}\frac{(2j-3)(2j-2)}{(2m+4j-7)(2m+4j-5)},

and real eigenvalues \alpha _{{1,d}}, \alpha _{{2,d}}, \dots, \alpha _{{d,d}}, arranged in ascending order of magnitude. Then

30.16.2 \alpha _{{j,d+1}}\leq\alpha _{{j,d}},

and

30.16.3 \mathop{\lambda^{{m}}_{{n}}\/}\nolimits\!\left(\gamma^{2}\right)=\lim _{{d\to\infty}}\alpha _{{p,d}}, p=\left\lfloor\frac{1}{2}(n-m)\right\rfloor+1.

The eigenvalues of \mathbf{A} can be computed by methods indicated in §§3.2(vi), 3.2(vii). The error satisfies

30.16.4 \alpha _{{p,d}}-\mathop{\lambda^{{m}}_{{n}}\/}\nolimits\!\left(\gamma^{2}\right)=\mathop{O\/}\nolimits\!\left(\frac{\gamma^{{4d}}}{4^{{2d+1}}((m+2d-1)!(m+2d+1)!)^{2}}\right), d\to\infty.

Example

For m=2, n=4, \gamma^{2}=10,

30.16.5
\alpha _{{2,2}}=14.18833\; 246,
\alpha _{{2,3}}=13.98002\; 0 13,
\alpha _{{2,4}}=13.97907\; 459,
\alpha _{{2,5}}=13.97907\; 345,
\alpha _{{2,6}}=13.97907\; 345,

which yields \mathop{\lambda^{{2}}_{{4}}\/}\nolimits\!\left(10\right)=13.97907\; 345. If n-m is odd, then (30.16.1) is replaced by

30.16.6
A_{{j,j}}=(m+2j-1)(m+2j)-2\gamma^{2}\*\frac{(m+2j-1)(m+2j)-1+m^{2}}{(2m+4j-3)(2m+4j+1)},
A_{{j,j+1}}=-\gamma^{2}\frac{(2m+2j)(2m+2j+1)}{(2m+4j+1)(2m+4j+3)},
A_{{j,j-1}}=-\gamma^{2}\frac{(2j-2)(2j-1)}{(2m+4j-5)(2m+4j-3)}.

§30.16(ii) Spheroidal Wave Functions of the First Kind

If |\gamma^{2}| is large, then we can use the asymptotic expansions referred to in §30.9 to approximate \mathop{\mathsf{Ps}^{{m}}_{{n}}\/}\nolimits\!\left(x,\gamma^{2}\right).

If \mathop{\lambda^{{m}}_{{n}}\/}\nolimits\!\left(\gamma^{2}\right) is known, then we can compute \mathop{\mathsf{Ps}^{{m}}_{{n}}\/}\nolimits\!\left(x,\gamma^{2}\right) (not normalized) by solving the differential equation (30.2.1) numerically with initial conditions w(0)=1, w^{{\prime}}(0)=0 if n-m is even, or w(0)=0, w^{{\prime}}(0)=1 if n-m is odd.

If \mathop{\lambda^{{m}}_{{n}}\/}\nolimits\!\left(\gamma^{2}\right) is known, then \mathop{\mathsf{Ps}^{{m}}_{{n}}\/}\nolimits\!\left(x,\gamma^{2}\right) can be found by summing (30.8.1). The coefficients a^{m}_{{n,r}}(\gamma^{2}) 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 \mathbf{A} be the d\times d matrix given by (30.16.1) if n-m is even, or by (30.16.6) if n-m is odd. Form the eigenvector [e_{{1,d}},e_{{2,d}},\dots,e_{{d,d}}]^{{\mathrm{T}}} of \mathbf{A} associated with the eigenvalue \alpha _{{p,d}}, p=\left\lfloor\frac{1}{2}(n-m)\right\rfloor+1, normalized according to

30.16.7 \sum _{{j=1}}^{d}e_{{j,d}}^{2}\frac{(n+m+2j-2p)!}{(n-m+2j-2p)!}\frac{1}{2n+4j-4p+1}=\frac{(n+m)!}{(n-m)!}\frac{1}{2n+1}.

Then

30.16.8 a_{{n,k}}^{m}(\gamma^{2})=\lim _{{d\to\infty}}e_{{k+p,d}},
30.16.9 \mathop{\mathsf{Ps}^{{m}}_{{n}}\/}\nolimits\!\left(x,\gamma^{2}\right)=\lim _{{d\to\infty}}\sum _{{j=1}}^{d}(-1)^{{j-p}}e_{{j,d}}\mathop{\mathsf{P}^{{m}}_{{n+2(j-p)}}\/}\nolimits\!\left(x\right).

For error estimates see Volkmer (2004a).

§30.16(iii) Radial Spheroidal Wave Functions

The coefficients a_{{n,k}}^{m}(\gamma^{2}) calculated in §30.16(ii) can be used to compute \mathop{S^{{m(j)}}_{{n}}\/}\nolimits\!\left(z,\gamma\right), j=1,2,3,4 from (30.11.3) as well as the connection coefficients K_{n}^{m}(\gamma) from (30.11.10) and (30.11.11).

For other methods see Van Buren and Boisvert (2002, 2004).