In numerical analysis , the Clenshaw algorithm , also called Clenshaw summation , is a recursive method to evaluate a linear combination of Chebyshev polynomials .[ 1] [ 2] The method was published by Charles William Clenshaw in 1955. It is a generalization of Horner's method for evaluating a linear combination of monomials .
It generalizes to more than just Chebyshev polynomials; it applies to any class of functions that can be defined by a three-term recurrence relation .[ 3]
In full generality, the Clenshaw algorithm computes the weighted sum of a finite series of functions
ϕ
k
(
x
)
{\displaystyle \phi _{k}(x)}
:
S
(
x
)
=
∑
k
=
0
n
a
k
ϕ
k
(
x
)
{\displaystyle S(x)=\sum _{k=0}^{n}a_{k}\phi _{k}(x)}
where
ϕ
k
,
k
=
0
,
1
,
…
{\displaystyle \phi _{k},\;k=0,1,\ldots }
is a sequence of functions that satisfy the linear recurrence relation
ϕ
k
+
1
(
x
)
=
α
k
(
x
)
ϕ
k
(
x
)
+
β
k
(
x
)
ϕ
k
−
1
(
x
)
,
{\displaystyle \phi _{k+1}(x)=\alpha _{k}(x)\,\phi _{k}(x)+\beta _{k}(x)\,\phi _{k-1}(x),}
where the coefficients
α
k
(
x
)
{\displaystyle \alpha _{k}(x)}
and
β
k
(
x
)
{\displaystyle \beta _{k}(x)}
are known in advance.
The algorithm is most useful when
ϕ
k
(
x
)
{\displaystyle \phi _{k}(x)}
are functions that are complicated to compute directly, but
α
k
(
x
)
{\displaystyle \alpha _{k}(x)}
and
β
k
(
x
)
{\displaystyle \beta _{k}(x)}
are particularly simple. In the most common applications,
α
(
x
)
{\displaystyle \alpha (x)}
does not depend on
k
{\displaystyle k}
, and
β
{\displaystyle \beta }
is a constant that depends on neither
x
{\displaystyle x}
nor
k
{\displaystyle k}
.
To perform the summation for given series of coefficients
a
0
,
…
,
a
n
{\displaystyle a_{0},\ldots ,a_{n}}
, compute the values
b
k
(
x
)
{\displaystyle b_{k}(x)}
by the "reverse" recurrence formula:
b
n
+
1
(
x
)
=
b
n
+
2
(
x
)
=
0
,
b
k
(
x
)
=
a
k
+
α
k
(
x
)
b
k
+
1
(
x
)
+
β
k
+
1
(
x
)
b
k
+
2
(
x
)
.
{\displaystyle {\begin{aligned}b_{n+1}(x)&=b_{n+2}(x)=0,\\b_{k}(x)&=a_{k}+\alpha _{k}(x)\,b_{k+1}(x)+\beta _{k+1}(x)\,b_{k+2}(x).\end{aligned}}}
Note that this computation makes no direct reference to the functions
ϕ
k
(
x
)
{\displaystyle \phi _{k}(x)}
. After computing
b
2
(
x
)
{\displaystyle b_{2}(x)}
and
b
1
(
x
)
{\displaystyle b_{1}(x)}
,
the desired sum can be expressed in terms of them and the simplest functions
ϕ
0
(
x
)
{\displaystyle \phi _{0}(x)}
and
ϕ
1
(
x
)
{\displaystyle \phi _{1}(x)}
:
S
(
x
)
=
ϕ
0
(
x
)
a
0
+
ϕ
1
(
x
)
b
1
(
x
)
+
β
1
(
x
)
ϕ
0
(
x
)
b
2
(
x
)
.
{\displaystyle S(x)=\phi _{0}(x)\,a_{0}+\phi _{1}(x)\,b_{1}(x)+\beta _{1}(x)\,\phi _{0}(x)\,b_{2}(x).}
See Fox and Parker[ 4] for more information and stability analyses.
Horner as a special case of Clenshaw [ edit ]
A particularly simple case occurs when evaluating a polynomial of the form
S
(
x
)
=
∑
k
=
0
n
a
k
x
k
.
{\displaystyle S(x)=\sum _{k=0}^{n}a_{k}x^{k}.}
The functions are simply
ϕ
0
(
x
)
=
1
,
ϕ
k
(
x
)
=
x
k
=
x
ϕ
k
−
1
(
x
)
{\displaystyle {\begin{aligned}\phi _{0}(x)&=1,\\\phi _{k}(x)&=x^{k}=x\phi _{k-1}(x)\end{aligned}}}
and are produced by the recurrence coefficients
α
(
x
)
=
x
{\displaystyle \alpha (x)=x}
and
β
=
0
{\displaystyle \beta =0}
.
In this case, the recurrence formula to compute the sum is
b
k
(
x
)
=
a
k
+
x
b
k
+
1
(
x
)
{\displaystyle b_{k}(x)=a_{k}+xb_{k+1}(x)}
and, in this case, the sum is simply
S
(
x
)
=
a
0
+
x
b
1
(
x
)
=
b
0
(
x
)
,
{\displaystyle S(x)=a_{0}+xb_{1}(x)=b_{0}(x),}
which is exactly the usual Horner's method .
Special case for Chebyshev series [ edit ]
Consider a truncated Chebyshev series
p
n
(
x
)
=
a
0
+
a
1
T
1
(
x
)
+
a
2
T
2
(
x
)
+
⋯
+
a
n
T
n
(
x
)
.
{\displaystyle p_{n}(x)=a_{0}+a_{1}T_{1}(x)+a_{2}T_{2}(x)+\cdots +a_{n}T_{n}(x).}
The coefficients in the recursion relation for the Chebyshev polynomials are
α
(
x
)
=
2
x
,
β
=
−
1
,
{\displaystyle \alpha (x)=2x,\quad \beta =-1,}
with the initial conditions
T
0
(
x
)
=
1
,
T
1
(
x
)
=
x
.
{\displaystyle T_{0}(x)=1,\quad T_{1}(x)=x.}
Thus, the recurrence is
b
k
(
x
)
=
a
k
+
2
x
b
k
+
1
(
x
)
−
b
k
+
2
(
x
)
{\displaystyle b_{k}(x)=a_{k}+2xb_{k+1}(x)-b_{k+2}(x)}
and the final sum is
p
n
(
x
)
=
a
0
+
x
b
1
(
x
)
−
b
2
(
x
)
.
{\displaystyle p_{n}(x)=a_{0}+xb_{1}(x)-b_{2}(x).}
One way to evaluate this is to continue the recurrence one more step, and compute
b
0
(
x
)
=
a
0
+
2
x
b
1
(
x
)
−
b
2
(
x
)
,
{\displaystyle b_{0}(x)=a_{0}+2xb_{1}(x)-b_{2}(x),}
(note the doubled a 0 coefficient) followed by
p
n
(
x
)
=
1
2
[
a
0
+
b
0
(
x
)
−
b
2
(
x
)
]
.
{\displaystyle p_{n}(x)={\tfrac {1}{2}}\left[a_{0}+b_{0}(x)-b_{2}(x)\right].}
Meridian arc length on the ellipsoid [ edit ]
Clenshaw summation is extensively used in geodetic applications.[ 2] A simple application is summing the trigonometric series to compute the meridian arc distance on the surface of an ellipsoid. These have the form
m
(
θ
)
=
C
0
θ
+
C
1
sin
θ
+
C
2
sin
2
θ
+
⋯
+
C
n
sin
n
θ
.
{\displaystyle m(\theta )=C_{0}\,\theta +C_{1}\sin \theta +C_{2}\sin 2\theta +\cdots +C_{n}\sin n\theta .}
Leaving off the initial
C
0
θ
{\displaystyle C_{0}\,\theta }
term, the remainder is a summation of the appropriate form. There is no leading term because
ϕ
0
(
θ
)
=
sin
0
θ
=
sin
0
=
0
{\displaystyle \phi _{0}(\theta )=\sin 0\theta =\sin 0=0}
.
The recurrence relation for
sin
k
θ
{\displaystyle \sin k\theta }
is
sin
(
k
+
1
)
θ
=
2
cos
θ
sin
k
θ
−
sin
(
k
−
1
)
θ
,
{\displaystyle \sin(k+1)\theta =2\cos \theta \sin k\theta -\sin(k-1)\theta ,}
making the coefficients in the recursion relation
α
k
(
θ
)
=
2
cos
θ
,
β
k
=
−
1.
{\displaystyle \alpha _{k}(\theta )=2\cos \theta ,\quad \beta _{k}=-1.}
and the evaluation of the series is given by
b
n
+
1
(
θ
)
=
b
n
+
2
(
θ
)
=
0
,
b
k
(
θ
)
=
C
k
+
2
cos
θ
b
k
+
1
(
θ
)
−
b
k
+
2
(
θ
)
,
f
o
r
n
≥
k
≥
1.
{\displaystyle {\begin{aligned}b_{n+1}(\theta )&=b_{n+2}(\theta )=0,\\b_{k}(\theta )&=C_{k}+2\cos \theta \,b_{k+1}(\theta )-b_{k+2}(\theta ),\quad \mathrm {for\ } n\geq k\geq 1.\end{aligned}}}
The final step is made particularly simple because
ϕ
0
(
θ
)
=
sin
0
=
0
{\displaystyle \phi _{0}(\theta )=\sin 0=0}
, so the end of the recurrence is simply
b
1
(
θ
)
sin
(
θ
)
{\displaystyle b_{1}(\theta )\sin(\theta )}
; the
C
0
θ
{\displaystyle C_{0}\,\theta }
term is added separately:
m
(
θ
)
=
C
0
θ
+
b
1
(
θ
)
sin
θ
.
{\displaystyle m(\theta )=C_{0}\,\theta +b_{1}(\theta )\sin \theta .}
Note that the algorithm requires only the evaluation of two trigonometric quantities
cos
θ
{\displaystyle \cos \theta }
and
sin
θ
{\displaystyle \sin \theta }
.
Difference in meridian arc lengths [ edit ]
Sometimes it necessary to compute the difference of two meridian arcs in a way that maintains high relative accuracy. This is accomplished by using trigonometric identities to write
m
(
θ
1
)
−
m
(
θ
2
)
=
C
0
(
θ
1
−
θ
2
)
+
∑
k
=
1
n
2
C
k
sin
(
1
2
k
(
θ
1
−
θ
2
)
)
cos
(
1
2
k
(
θ
1
+
θ
2
)
)
.
{\displaystyle m(\theta _{1})-m(\theta _{2})=C_{0}(\theta _{1}-\theta _{2})+\sum _{k=1}^{n}2C_{k}\sin {\bigl (}{\textstyle {\frac {1}{2}}}k(\theta _{1}-\theta _{2}){\bigr )}\cos {\bigl (}{\textstyle {\frac {1}{2}}}k(\theta _{1}+\theta _{2}){\bigr )}.}
Clenshaw summation can be applied in this case[ 5]
provided we simultaneously compute
m
(
θ
1
)
+
m
(
θ
2
)
{\displaystyle m(\theta _{1})+m(\theta _{2})}
and perform a matrix summation,
M
(
θ
1
,
θ
2
)
=
[
(
m
(
θ
1
)
+
m
(
θ
2
)
)
/
2
(
m
(
θ
1
)
−
m
(
θ
2
)
)
/
(
θ
1
−
θ
2
)
]
=
C
0
[
μ
1
]
+
∑
k
=
1
n
C
k
F
k
(
θ
1
,
θ
2
)
,
{\displaystyle {\mathsf {M}}(\theta _{1},\theta _{2})={\begin{bmatrix}(m(\theta _{1})+m(\theta _{2}))/2\\(m(\theta _{1})-m(\theta _{2}))/(\theta _{1}-\theta _{2})\end{bmatrix}}=C_{0}{\begin{bmatrix}\mu \\1\end{bmatrix}}+\sum _{k=1}^{n}C_{k}{\mathsf {F}}_{k}(\theta _{1},\theta _{2}),}
where
δ
=
1
2
(
θ
1
−
θ
2
)
,
μ
=
1
2
(
θ
1
+
θ
2
)
,
F
k
(
θ
1
,
θ
2
)
=
[
cos
k
δ
sin
k
μ
sin
k
δ
δ
cos
k
μ
]
.
{\displaystyle {\begin{aligned}\delta &={\tfrac {1}{2}}(\theta _{1}-\theta _{2}),\\[1ex]\mu &={\tfrac {1}{2}}(\theta _{1}+\theta _{2}),\\[1ex]{\mathsf {F}}_{k}(\theta _{1},\theta _{2})&={\begin{bmatrix}\cos k\delta \sin k\mu \\{\dfrac {\sin k\delta }{\delta }}\cos k\mu \end{bmatrix}}.\end{aligned}}}
The first element of
M
(
θ
1
,
θ
2
)
{\displaystyle {\mathsf {M}}(\theta _{1},\theta _{2})}
is the average
value of
m
{\displaystyle m}
and the second element is the average slope.
F
k
(
θ
1
,
θ
2
)
{\displaystyle {\mathsf {F}}_{k}(\theta _{1},\theta _{2})}
satisfies the recurrence
relation
F
k
+
1
(
θ
1
,
θ
2
)
=
A
(
θ
1
,
θ
2
)
F
k
(
θ
1
,
θ
2
)
−
F
k
−
1
(
θ
1
,
θ
2
)
,
{\displaystyle {\mathsf {F}}_{k+1}(\theta _{1},\theta _{2})={\mathsf {A}}(\theta _{1},\theta _{2}){\mathsf {F}}_{k}(\theta _{1},\theta _{2})-{\mathsf {F}}_{k-1}(\theta _{1},\theta _{2}),}
where
A
(
θ
1
,
θ
2
)
=
2
[
cos
δ
cos
μ
−
δ
sin
δ
sin
μ
−
sin
δ
δ
sin
μ
cos
δ
cos
μ
]
{\displaystyle {\mathsf {A}}(\theta _{1},\theta _{2})=2{\begin{bmatrix}\cos \delta \cos \mu &-\delta \sin \delta \sin \mu \\-\displaystyle {\frac {\sin \delta }{\delta }}\sin \mu &\cos \delta \cos \mu \end{bmatrix}}}
takes the place of
α
{\displaystyle \alpha }
in the recurrence relation, and
β
=
−
1
{\displaystyle \beta =-1}
.
The standard Clenshaw algorithm can now be applied to yield
B
n
+
1
=
B
n
+
2
=
0
,
B
k
=
C
k
I
+
A
B
k
+
1
−
B
k
+
2
,
f
o
r
n
≥
k
≥
1
,
M
(
θ
1
,
θ
2
)
=
C
0
[
μ
1
]
+
B
1
F
1
(
θ
1
,
θ
2
)
,
{\displaystyle {\begin{aligned}{\mathsf {B}}_{n+1}&={\mathsf {B}}_{n+2}={\mathsf {0}},\\[1ex]{\mathsf {B}}_{k}&=C_{k}{\mathsf {I}}+{\mathsf {A}}{\mathsf {B}}_{k+1}-{\mathsf {B}}_{k+2},\qquad \mathrm {for\ } n\geq k\geq 1,\\[1ex]{\mathsf {M}}(\theta _{1},\theta _{2})&=C_{0}{\begin{bmatrix}\mu \\1\end{bmatrix}}+{\mathsf {B}}_{1}{\mathsf {F}}_{1}(\theta _{1},\theta _{2}),\end{aligned}}}
where
B
k
{\displaystyle {\mathsf {B}}_{k}}
are 2×2 matrices. Finally we have
m
(
θ
1
)
−
m
(
θ
2
)
θ
1
−
θ
2
=
M
2
(
θ
1
,
θ
2
)
.
{\displaystyle {\frac {m(\theta _{1})-m(\theta _{2})}{\theta _{1}-\theta _{2}}}={\mathsf {M}}_{2}(\theta _{1},\theta _{2}).}
This technique can be used in the limit
θ
2
=
θ
1
=
μ
{\displaystyle \theta _{2}=\theta _{1}=\mu }
and
δ
=
0
{\displaystyle \delta =0}
to simultaneously compute
m
(
μ
)
{\displaystyle m(\mu )}
and the derivative
d
m
(
μ
)
/
d
μ
{\displaystyle dm(\mu )/d\mu }
, provided that, in evaluating
F
1
{\displaystyle {\mathsf {F}}_{1}}
and
A
{\displaystyle {\mathsf {A}}}
, we take
lim
δ
→
0
(
sin
k
δ
)
/
δ
=
k
{\displaystyle \lim _{\delta \to 0}(\sin k\delta )/\delta =k}
.
^ Clenshaw, C. W. (July 1955). "A note on the summation of Chebyshev series" . Mathematical Tables and Other Aids to Computation . 9 (51): 118. doi :10.1090/S0025-5718-1955-0071856-0 . ISSN 0025-5718 . Note that this paper is written in terms of the Shifted Chebyshev polynomials of the first kind
T
n
∗
(
x
)
=
T
n
(
2
x
−
1
)
{\displaystyle T_{n}^{*}(x)=T_{n}(2x-1)}
.
^ a b Tscherning, C. C.; Poder, K. (1982), "Some Geodetic applications of Clenshaw Summation" (PDF) , Bolletino di Geodesia e Scienze Affini , 41 (4): 349– 375, archived from the original (PDF) on 2007-06-12, retrieved 2012-08-02
^ Press, WH; Teukolsky, SA; Vetterling, WT; Flannery, BP (2007), "Section 5.4.2. Clenshaw's Recurrence Formula" , Numerical Recipes: The Art of Scientific Computing (3rd ed.), New York: Cambridge University Press, ISBN 978-0-521-88068-8
^ Fox, Leslie; Parker, Ian B. (1968), Chebyshev Polynomials in Numerical Analysis , Oxford University Press, ISBN 0-19-859614-6
^ Karney, C. F. F. (2024). "The area of rhumb polygons" . Stud. Geophys. Geod . 68 (3– 4): 99– 120. doi :10.1007/s11200-024-0709-z Appendix B {{cite journal }}
: CS1 maint: postscript (link )