Shanks transformation

In numerical analysis, the Shanks transformation is a non-linear series acceleration method to increase the rate of convergence of a sequence. This method is named after Daniel Shanks, who rediscovered this sequence transformation in 1955. It was first derived and published by R. Schmidt in 1941.[1]

One can calculate only a few terms of a perturbation expansion, usually no more than two or three, and almost never more than seven. The resulting series is often slowly convergent, or even divergent. Yet those few terms contain a remarkable amount of information, which the investigator should do his best to extract.
This viewpoint has been persuasively set forth in a delightful paper by Shanks (1955), who displays a number of amazing examples, including several from fluid mechanics.

Milton D. Van Dyke (1975) Perturbation methods in fluid mechanics, p. 202.

Contents

Shanks transformation

Formulation

For a sequence \left\{a_m\right\}_{k\in\mathbb{N}} the series

A = \sum_{m=0}^\infty a_m\,

is to be determined. First, the partial sum A_n is defined as:

A_n = \sum_{m=0}^n a_m\,

and forms a new sequence \left\{A_n\right\}_{n\in\mathbb{N}}. Provided the series converges, A_n will approach in the limit to A as n\to\infty. The Shanks transformation S(A_n) of the sequence A_n is defined as[2][3]

S(A_n) = \frac{A_{n%2B1}\, A_{n-1}\, -\, A_n^2}{A_{n%2B1}-2A_n%2BA_{n-1}}

and forms a new sequence. The sequence S(A_n) often converges more rapidly than the sequence A_n. Further speed-up may be obtained by repeated use of the Shanks transformation, by computing S^2(A_n)=S(S(A_n)), S^3(A_n)=S(S(S(A_n))), etc.

Note that the non-linear transformation as used in the Shanks transformation is of similar form as used in Aitken's delta-squared process. But while Aitken's method operates on the coefficients \left\{a_m\right\} of the original sequence, the Shanks transformation operates on the partial sums A_n.

Example

As an example, consider the slowly-convergent series[3]

4 \sum_{k=0}^\infty (-1)^k \frac{1}{2k%2B1} = 4 \left( 1 - \frac{1}{3} %2B \frac{1}{5} - \frac{1}{7} %2B \cdots \right)

which has the exact sum π ≈ 3.14159265. The partial sum A_6 has only one digit accuracy, while six-figure accuracy requires summing about 400,000 terms.

In the table below, the partial sums A_n, the Shanks transformation S(A_n) on them, as well as the repeated Shanks transformations S^2(A_n) and S^3(A_n) are given for n up to 12. The figure to the right shows the absolute error for the partial sums and Shanks transformation results, clearly showing the improved accuracy and convergence rate.

n A_n S(A_n) S^2(A_n) S^3(A_n)
0 4.00000000 — — —
1 2.66666667 3.16666667 — —
2 3.46666667 3.13333333 3.14210526 —
3 2.89523810 3.14523810 3.14145022 3.14159936
4 3.33968254 3.13968254 3.14164332 3.14159086
5 2.97604618 3.14271284 3.14157129 3.14159323
6 3.28373848 3.14088134 3.14160284 3.14159244
7 3.01707182 3.14207182 3.14158732 3.14159274
8 3.25236593 3.14125482 3.14159566 3.14159261
9 3.04183962 3.14183962 3.14159086 3.14159267
10 3.23231581 3.14140672 3.14159377 3.14159264
11 3.05840277 3.14173610 3.14159192 3.14159266
12 3.21840277 3.14147969 3.14159314 3.14159265

The Shanks transformation S(A_1) already has two-digit accuracy, while the original partial sums only establish the same accuracy at A_{24}. Remarkably, S^3(A_3) has six digits accuracy, obtained from repeated Shank transformations applied to the first seven terms A_0 till A_6. As said before, A_n only obtains 6-digit accuracy after about summing 400,000 terms.

Motivation

The Shanks transformation is motivated by the observation that — for larger n — the partial sum A_n quite often behaves approximately as[2]

A_n = A %2B \alpha q^n, \,

with |q|<1 so that the sequence converges transiently to the series result A for n\to\infty. So for n-1, n and n%2B1 the respective partial sums are:

A_{n-1} = A %2B \alpha q^{n-1} \quad , \qquad A_n = A %2B \alpha q^n \qquad \text{and} \qquad A_{n%2B1} = A %2B \alpha q^{n%2B1}.

These three equations contain three unknowns: A, \alpha and q. Solving for A gives[2]

A = \frac{A_{n%2B1}\, A_{n-1}\, -\, A_n^2}{A_{n%2B1}-2A_n%2BA_{n-1}}.

In the (exceptional) case that the denominator is equal to zero: then A_n=A for all n.

Generalized Shanks transformation

The generalized kth-order Shanks transformation is given as the ratio of the determinants:[4]

S_k(A_n) = \frac{ \begin{vmatrix} A_{n-k} & \cdots & A_{n-1} & A_n \\ \Delta A_{n-k} & \cdots & \Delta A_{n-1} & \Delta A_{n} \\ \Delta A_{n-k%2B1} & \cdots & \Delta A_{n} & \Delta A_{n%2B1} \\ \vdots & & \vdots & \vdots \\ \Delta A_{n-1} & \cdots & \Delta A_{n%2Bk-2} & \Delta A_{n%2Bk-1} \\ \end{vmatrix} }{ \begin{vmatrix} 1 & \cdots & 1 & 1 \\ \Delta A_{n-k} & \cdots & \Delta A_{n-1} & \Delta A_{n} \\ \Delta A_{n-k%2B1} & \cdots & \Delta A_{n} & \Delta A_{n%2B1} \\ \vdots & & \vdots & \vdots \\ \Delta A_{n-1} & \cdots & \Delta A_{n%2Bk-2} & \Delta A_{n%2Bk-1} \\ \end{vmatrix} },

with \Delta A_p = A_{p%2B1} - A_p. It is the solution of a model for the convergence behaviour of the partial sums A_n with k distinct transients:

A_n = A %2B \sum_{p=1}^k \alpha_p q_p^n.

This model for the convergence behaviour contains 2k%2B1 unknowns. By evaluating the above equation at the elements A_{n-k}, A_{n-k%2B1}, \cdots, A_{n%2Bk} and solving for A, the above expression for the kth-order Shanks transformation is obtained. The first-order generalized Shanks transformation is equal to the ordinary Shanks transformation: S_1(A_n)=S(A_n).

The generalized Shanks transformation is closely related to Padé approximants and Padé tables.[4]

See also

Notes

  1. ^ Weniger (2003).
  2. ^ a b c Bender & Orszag (1999), pp. 368–375.
  3. ^ a b Van Dyke (1975), pp. 202–205.
  4. ^ a b Bender & Orszag (1999), pp. 389–392.

References