C06 Chapter Introduction : NAG Library CL Interface, Mark 30

This chapter is concerned with the following tasks:

(a)calculating the discrete Fourier transform of a sequence of real or complex data values;
(b)calculating the discrete convolution or the discrete correlation of two sequences of real or complex data values using discrete Fourier transforms;
(c)calculating the fast Gauss transform approximation to the discrete Gauss transform;
(d)direct summation of orthogonal series;

Most of the functions in this chapter calculate the finite discrete Fourier transform (DFT) of a sequence of

n

complex numbers

z_{j}

, for

j = 0, 1, \dots, n - 1

. The direct transform is defined by

{\hat{z}}_{k} = \frac{1}{\sqrt{n}} \sum_{j = 0}^{n - 1} z_{j} \exp (- i \frac{2 π j k}{n})

(1)

for

k = 0, 1, \dots, n - 1

. Note that equation (1) makes sense for all integral

k

and with this extension

{\hat{z}}_{k}

is periodic with period

n

, i.e.,

{\hat{z}}_{k} = {\hat{z}}_{k \pm n}

, and in particular

{\hat{z}}_{- k} = {\hat{z}}_{n - k}

. Note also that the scale-factor of

\frac{1}{\sqrt{n}}

may be omitted in the definition of the DFT, and replaced by

\frac{1}{n}

in the definition of the inverse.

If we write

z_{j} = x_{j} + i y_{j}

and

{\hat{z}}_{k} = a_{k} + i b_{k}

, then the definition of

{\hat{z}}_{k}

may be written in terms of sines and cosines as

a_{k} = \frac{1}{\sqrt{n}} \sum_{j = 0}^{n - 1} (x_{j} \cos (\frac{2 π j k}{n}) + y_{j} \sin (\frac{2 π j k}{n}))

b_{k} = \frac{1}{\sqrt{n}} \sum_{j = 0}^{n - 1} (y_{j} \cos (\frac{2 π j k}{n}) - x_{j} \sin (\frac{2 π j k}{n})) .

The original data values

z_{j}

may conversely be recovered from the transform

{\hat{z}}_{k}

by an inverse discrete Fourier transform:

z_{j} = \frac{1}{\sqrt{n}} \sum_{k = 0}^{n - 1} {\hat{z}}_{k} \exp (+ i \frac{2 π j k}{n})

(2)

for

j = 0, 1, \dots, n - 1

. If we take the complex conjugate of (2), we find that the sequence

{\bar{z}}_{j}

is the DFT of the sequence

{\bar{\hat{z}}}_{k}

. Hence the inverse DFT of the sequence

{\hat{z}}_{k}

may be obtained by taking the complex conjugates of the

{\hat{z}}_{k}

; performing a DFT, and taking the complex conjugates of the result. (Note that the terms forward transform and backward transform are also used to mean the direct and inverse transforms respectively.)

The definition (1) of a one-dimensional transform can easily be extended to multidimensional transforms. For example, in two dimensions we have

{\hat{z}}_{k_{1} k_{2}} = \frac{1}{\sqrt{n_{1} n_{2}}} \sum_{j_{1} = 0}^{n_{1} - 1} \sum_{j_{2} = 0}^{n_{2} - 1} z_{j_{1} j_{2}} \exp (- i \frac{2 π j_{1} k_{1}}{n_{1}}) \exp (- i \frac{2 π j_{2} k_{2}}{n_{2}}) .

(3)

Note: definitions of the discrete Fourier transform vary. Sometimes (2) is used as the definition of the DFT, and (1) as the definition of the inverse.

If the original sequence is purely real valued, i.e.,

z_{j} = x_{j}

, then

{\hat{z}}_{k} = a_{k} + i b_{k} = \frac{1}{\sqrt{n}} \sum_{j = 0}^{n - 1} x_{j} \exp (- i \frac{2 π j k}{n})

and

{\hat{z}}_{n - k}

is the complex conjugate of

{\hat{z}}_{k}

. Thus the DFT of a real sequence is a particular type of complex sequence, called a Hermitian sequence, or half-complex or conjugate symmetric, with the properties

a_{n - k} = a_{k} b_{n - k} = {- b}_{k} b_{0} = 0

and, if

n

is even,

b_{n / 2} = 0

.

Thus a Hermitian sequence of

n

complex data values can be represented by only

n

, rather than

2 n

, independent real values. This can obviously lead to economies in storage, with two schemes being used in this chapter. In the first (deprecated) scheme, which will be referred to as the real storage format for Hermitian sequences, the real parts

a_{k}

for

0 \leq k \leq n / 2

are stored in normal order in the first

n / 2 + 1

locations of an array x of length

n

; the corresponding nonzero imaginary parts are stored in reverse order in the remaining locations of x. To clarify, the following two tables illustrate the storage of the real and imaginary parts of

{\hat{z}}_{k}

for the two cases:

n

even and

n

odd.

If

n

is even then the sequence has two purely real elements and is stored as follows:

Index of x	$0$	$1$	$2$	$\dots$	$n / 2$	$\dots$	$n - 2$	$n - 1$
Sequence	$a_{0}$	$a_{1} + i b_{1}$	$a_{2} + i b_{2}$	$\dots$	$a_{n / 2}$	$\dots$	$a_{2} - i b_{2}$	$a_{1} - i b_{1}$
Stored values	$a_{0}$	$a_{1}$	$a_{2}$	$\dots$	$a_{n / 2}$	$\dots$	$b_{2}$	$b_{1}$

\begin{matrix} x [k] = a_{k}, & for ​ k = 0, 1, \dots, n / 2, and \\ x [n - k] = b_{k}, & for ​ k = 1, 2, \dots, n / 2 - 1 . \end{matrix}

If

n

is odd then the sequence has one purely real element and, letting

n = 2 s + 1

, is stored as follows:

Index of x	$0$	$1$	$2$	$\dots$	$s$	$s + 1$	$\dots$	$n - 2$	$n - 1$
Sequence	$a_{0}$	$a_{1} + i b_{1}$	$a_{2} + i b_{2}$	$\dots$	$a_{s} + i b_{s}$	$a_{s} - i b_{s}$	$\dots$	$a_{2} - i b_{2}$	$a_{1} - i b_{1}$
Stored values	$a_{0}$	$a_{1}$	$a_{2}$	$\dots$	$a_{s}$	$b_{s}$	$\dots$	$b_{2}$	$b_{1}$

\begin{matrix} x [k] = a_{k}, & for ​ k = 0, 1, \dots, s, and \\ x [n - k] = b_{k}, & for ​ k = 1, 2, \dots, s . \end{matrix}

The second (recommended) storage scheme, referred to in this chapter as the complex storage format for Hermitian sequences, stores the real and imaginary parts

a_{k}, b_{k}

, for

0 \leq k \leq n / 2

, in consecutive locations of an array x of length

n + 2

. The following two tables illustrate the storage of the real and imaginary parts of

{\hat{z}}_{k}

for the two cases:

n

even and

n

odd.

If

n

is even then the sequence has two purely real elements and is stored as follows:

Index of x	$0$	$1$	$2$	$3$	$\dots$	$n - 2$	$n - 1$	$n$	$n + 1$
Stored values	$a_{0}$	$b_{0} = 0$	$a_{1}$	$b_{1}$	$\dots$	$a_{n / 2 - 1}$	$b_{n / 2 - 1}$	$a_{n / 2}$	$b_{n / 2} = 0$

\begin{matrix} x [2 \times k - 1] = a_{k}, & for ​ k = 0, 1, \dots, n / 2, and \\ x [2 \times k + 1 - 1] = b_{k}, & for ​ k = 0, 1, \dots, n / 2 . \end{matrix}

If

n

is odd then the sequence has one purely real element and, letting

n = 2 s + 1

, is stored as follows:

Index of x	$0$	$1$	$2$	$3$	$\dots$	$n - 2$	$n - 1$	$n$	$n + 1$
Stored values	$a_{0}$	$b_{0} = 0$	$a_{1}$	$b_{1}$	$\dots$	$b_{s - 1}$	$a_{s}$	$b_{s}$	$0$

\begin{matrix} x [2 \times k - 1] = a_{k}, & for ​ k = 0, 1, \dots, s, and \\ x [2 \times k + 1 - 1] = b_{k}, & for ​ k = 0, 1, \dots, s . \end{matrix}

Also, given a Hermitian sequence, the inverse (or backward) discrete transform produces a real sequence. That is,

x_{j} = \frac{1}{\sqrt{n}} (a_{0} + 2 \sum_{k = 1}^{n / 2 - 1} (a_{k} \cos (\frac{2 π j k}{n}) - b_{k} \sin (\frac{2 π j k}{n})) + a_{n / 2})

where

a_{n / 2} = 0

if

n

is odd.

For real data that is two-dimensional or higher, the symmetry in the transform persists for the leading dimension only. So, using the notation of equation (3) for the complex two-dimensional discrete transform, we have that

{\hat{z}}_{k_{1} k_{2}}

is the complex conjugate of

{\hat{z}}_{(n_{1} - k_{1}) (n_{2} - k_{2})}

. It is more convenient for transformed data of two or more dimensions to be stored as a complex sequence of length

(n_{1} / 2 + 1) \times n_{2} \times \dots \times n_{d}

where

d

is the number of dimensions. The inverse discrete Fourier transform operating on such a complex sequence (Hermitian in the leading dimension) returns a real array of full dimension (

n_{1} \times n_{2} \times \dots \times n_{d}

).

In many applications the sequence

x_{j}

will not only be real, but may also possess additional symmetries which we may exploit to reduce further the computing time and storage requirements. For example, if the sequence

x_{j}

is odd,

(x_{j} = {- x}_{n - j})

, then the discrete Fourier transform of

x_{j}

contains only sine terms. Rather than compute the transform of an odd sequence, we define the sine transform of a real sequence by

{\hat{x}}_{k} = \sqrt{\frac{2}{n}} \sum_{j = 1}^{n - 1} x_{j} \sin (\frac{π j k}{n}),

which could have been computed using the Fourier transform of a real odd sequence of length

2 n

. In this case the

x_{j}

are arbitrary, and the symmetry only becomes apparent when the sequence is extended. Similarly we define the cosine transform of a real sequence by

{\hat{x}}_{k} = \sqrt{\frac{2}{n}} (\frac{1}{2} x_{0} + \sum_{j = 1}^{n - 1} x_{j} \cos (\frac{π j k}{n}) + \frac{1}{2} {(- 1)}^{k} x_{n})

which could have been computed using the Fourier transform of a real even sequence of length

2 n

.

In addition to these ‘half-wave’ symmetries described above, sequences arise in practice with ‘quarter-wave’ symmetries. We define the quarter-wave sine transform by

{\hat{x}}_{k} = \frac{1}{\sqrt{n}} (\sum_{j = 1}^{n - 1} x_{j} \sin (\frac{π j (2 k - 1)}{2 n}) + \frac{1}{2} {(- 1)}^{k - 1} x_{n})

which could have been computed using the Fourier transform of a real sequence of length

4 n

of the form

(0, x_{1}, \dots, x_{n}, x_{n - 1}, \dots, x_{1}, 0, {- x}_{1}, \dots, {- x}_{n}, {- x}_{n - 1}, \dots, {- x}_{1}) .

Similarly we may define the quarter-wave cosine transform by

{\hat{x}}_{k} = \frac{1}{\sqrt{n}} (\frac{1}{2} x_{0} + \sum_{j = 1}^{n - 1} x_{j} \cos (\frac{π j (2 k - 1)}{2 n}))

which could have been computed using the Fourier transform of a real sequence of length

4 n

of the form

(x_{0}, x_{1}, \dots, x_{n - 1}, 0, {- x}_{n - 1}, \dots, {- x}_{0}, {- x}_{1}, \dots, {- x}_{n - 1}, 0, x_{n - 1}, \dots, x_{1}) .

The usual application of the discrete Fourier transform is that of obtaining an approximation of the Fourier integral transform

F (s) = \int_{- \infty}^{\infty} f (t) \exp (- i 2 π s t) d t

when

f (t)

is negligible outside some region

(0, c)

. Dividing the region into

n

equal intervals we have

F (s) ≅ \frac{c}{n} \sum_{j = 0}^{n - 1} f_{j} \exp (\frac{- i 2 π s j c}{n})

and so

F_{k} ≅ \frac{c}{n} \sum_{j = 0}^{n - 1} f_{j} \exp (\frac{- i 2 π j k}{n})

for

k = 0, 1, \dots, n - 1

, where

f_{j} = f (j c / n)

and

F_{k} = F (k / c)

.

Hence the discrete Fourier transform gives an approximation to the Fourier integral transform in the region

s = 0

to

s = n / c

.

If the function

f (t)

is defined over some more general interval

(a, b)

, then the integral transform can still be approximated by the discrete transform provided a shift is applied to move the point

a

to the origin.

One of the most important applications of the discrete Fourier transform is to the computation of the discrete convolution or correlation of two vectors

x

and

y

defined (as in Brigham (1974)) by

convolution: $z_{k} = \sum_{j = 0}^{n - 1} x_{j} y_{k - j}$
correlation: $w_{k} = \sum_{j = 0}^{n - 1} {\bar{x}}_{j} y_{k + j}$

(Here

x

and

y

are assumed to be periodic with period

n

.)

Under certain circumstances (see Brigham (1974)) these can be used as approximations to the convolution or correlation integrals defined by

z (s) = \int_{- \infty}^{\infty} x (t) y (s - t) d t

and

w (s) = \int_{- \infty}^{\infty} \bar{x} (t) y (s + t) d t, - \infty < s < \infty .

For more general advice on the use of Fourier transforms, see Hamming (1962); more detailed information on the fast Fourier transform algorithm can be found in Gentleman and Sande (1966) and Brigham (1974).

A further application of the fast Fourier transform, and in particular of the Fourier transforms of symmetric sequences, is in the solution of elliptic PDEs. If an equation is discretized using finite differences, then it is possible to reduce the problem of solving the resulting large system of linear equations to that of solving a number of tridiagonal systems of linear equations. This is accomplished by uncoupling the equations using Fourier transforms, where the nature of the boundary conditions determines the choice of transforms – see Section 3.3. Full details of the Fourier method for the solution of PDEs may be found in Swarztrauber (1977) and Swarztrauber (1984).

Gauss transforms have applications in areas including statistics, machine learning, and numerical solution of the heat equation. The discrete Gauss transform (DGT),

G (y)

, evaluated at a set of target points

y (j)

, for

j = 1, 2, \dots, m \in ℝ^{d}

, is defined as:

G (y_{j}) = \sum_{i = 1}^{n} q_{i} e^{- {‖ y_{j} - x_{i} ‖}_{2}^{2} / h_{i}^{2}}, j = 1, \dots, m

where

x_{i}

, for

i = 1, 2, \dots, n \in ℝ^{d}

, are the Gaussian source points,

q_{i}

, for

i = 1, 2, \dots, n \in ℝ^{+}

, are the source weights and

h_{i}

, for

i = 1, 2, \dots, n \in ℝ^{+}

, are the source standard deviations (alternatively source scales or source bandwidths).

The fast Gauss transform (FGT) algorithm presented in Raykar and Duraiswami (2005) approximates the DGT by using two Taylor series and clustering of the source points.

For any series of functions

ϕ_{i}

which satisfy a recurrence

ϕ_{r + 1} (x) + α_{r} (x) ϕ_{r} (x) + β_{r} (x) ϕ_{r - 1} (x) = 0

the sum

\sum_{r = 0}^{n} a_{r} ϕ_{r} (x)

is given by

\sum_{r = 0}^{n} a_{r} ϕ_{r} (x) = b_{0} (x) ϕ_{0} (x) + b_{1} (x) (ϕ_{1} (x) + α_{0} (x) ϕ_{0} (x))

where

b_{r} (x) + α_{r} (x) b_{r + 1} (x) + β_{r + 1} (x) b_{r + 2} (x) = a_{r} b_{n + 1} (x) = b_{n + 2} (x) = 0 .

This may be used to compute the sum of the series. For further reading, see Hamming (1962).

The fast Fourier transform algorithm ceases to be ‘fast’ if applied to values of

n

which cannot be expressed as a product of small prime factors. All the FFT functions in this chapter are particularly efficient if the only prime factors of

n

are

2

,

3

or

5

.

The choice of function is determined first of all by whether the data values constitute a real, Hermitian or general complex sequence. It is wasteful of time and storage to use an inappropriate function.

c06pac transforms a single sequence of real data onto (and in-place) a representation of the transformed Hermitian sequence using the complex storage scheme described in Section 2.1.2. c06pac also performs the inverse transform using the representation of Hermitian data and transforming back to a real data sequence.

Alternatively, the two-dimensional function c06pvc can be used (on setting the second dimension to 1) to transform a sequence of real data onto an Hermitian sequence whose first half is stored in a separate Complex array. The second half need not be stored since these are the complex conjugate of the first half in reverse order. c06pwc performs the inverse operation, transforming the Hermitian sequence (half-)stored in a Complex array onto a separate real array.

Multiple sequences of real data can also be transformed to and from a transformed Hermitian sequence (using the complex storage scheme). c06ppc assumes that the original real data is stored such that all the corresponding (

j

-th say) elements of the different streams are stored contiguously. c06pqc assumes that the elements of each sequence are stored contiguously; this is the recommended form of storage since it normally results in more efficient computation.

c06pcc transforms a single complex sequence in-place; it also performs the inverse transform. c06psc transforms multiple complex sequences, each stored sequentially; it also performs the inverse transform on multiple complex sequences. This function is designed to perform several transforms in a single call, all with the same value of

n

.

If extensive use is to be made of these functions and you are concerned about efficiency, you are advised to conduct your own timing tests.

Four functions are provided for computing fast Fourier transforms (FFTs) of real symmetric sequences. c06rec computes multiple Fourier sine transforms, c06rfc computes multiple Fourier cosine transforms, c06rgc computes multiple quarter-wave Fourier sine transforms, and c06rhc computes multiple quarter-wave Fourier cosine transforms.

As described in Section 2.1.6, Fourier transforms may be used in the solution of elliptic PDEs.

c06rec may be used to solve equations where the solution is specified along the boundary.

c06rfc may be used to solve equations where the derivative of the solution is specified along the boundary.

c06rgc may be used to solve equations where the solution is specified on the lower boundary, and the derivative of the solution is specified on the upper boundary.

c06rhc may be used to solve equations where the derivative of the solution is specified on the lower boundary, and the solution is specified on the upper boundary.

For equations with periodic boundary conditions the full-range Fourier transforms computed by c06pac are appropriate.

The following functions compute multidimensional discrete Fourier transforms of real, Hermitian and complex data stored in Complex arrays:

	double	Hermitian	Complex
$2$ dimensions	c06pvc	c06pwc	c06puc
$3$ dimensions	c06pyc	c06pzc	c06pxc
any number of dimensions			c06pjc

The Hermitian data, either transformed from or being transformed to real data, is compacted (due to symmetry) along its first dimension when stored in Complex arrays; thus approximately half the full Hermitian data is stored.

c06puc and c06pxc should be used in preference to c06pjc for two- and three-dimensional transforms, as they are easier to use and are likely to be more efficient.

The transform of multidimensional real data is stored as a complex sequence that is Hermitian in its leading dimension. The inverse transform takes such a complex sequence and computes the real transformed sequence. Consequently, separate functions are provided for performing forward and inverse transforms.

c06pvc performs the forward two-dimensionsal transform while c06pwc performs the inverse of this transform.

c06pyc performs the forward three-dimensional transform while c06pzc performs the inverse of this transform.

The complex sequences computed by c06pvc and c06pyc contain roughly half of the Fourier coefficients; the remainder can be reconstructed by conjugation of those computed. For example, the Fourier coefficients of the two-dimensional transform

{\hat{z}}_{(n_{1} - k_{1}) k_{2}}

are the complex conjugate of

{\hat{z}}_{k_{1} k_{2}}

for

k_{1} = 0, 1, \dots, n_{1} / 2

, and

k_{2} = 0, 1, \dots, n_{2} - 1

.

c06fkc computes either the discrete convolution or the discrete correlation of two real vectors.

The only function available is c06sac. If the dimensionality of the data is low or the number of source and target points is small, however, it may be more efficient to evaluate the discrete Gauss transform directly.

The only function available is c06dcc, which sums a finite Chebyshev series

\sum_{j = 0}^{n} c_{j} T_{j} (x), \sum_{j = 0}^{n} c_{j} T_{2 j} (x) or \sum_{j = 0}^{n} c_{j} T_{2 j + 1} (x)

depending on the choice of argument.

None.

The following lists all those functions that have been withdrawn since Mark 24 of the Library or are in the Library, but deprecated.

Brigham E O (1974) The Fast Fourier Transform Prentice–Hall

Davies S B and Martin B (1979) Numerical inversion of the Laplace transform: A survey and comparison of methods J. Comput. Phys. 33 1–32

Fox L and Parker I B (1968) Chebyshev Polynomials in Numerical Analysis Oxford University Press

Gentleman W S and Sande G (1966) Fast Fourier transforms for fun and profit Proc. Joint Computer Conference, AFIPS 29 563–578

Hamming R W (1962) Numerical Methods for Scientists and Engineers McGraw–Hill

Raykar V C and Duraiswami R (2005) Improved Fast Gauss Transform With Variable Source Scales University of Maryland Technical Report CS-TR-4727/UMIACS-TR-2005-34

Shanks D (1955) Nonlinear transformations of divergent and slowly convergent sequences J. Math. Phys. 34 1–42

Swarztrauber P N (1977) The methods of cyclic reduction, Fourier analysis and the FACR algorithm for the discrete solution of Poisson's equation on a rectangle SIAM Rev. 19(3) 490–501

Swarztrauber P N (1984) Fast Poisson solvers Studies in Numerical Analysis (ed G H Golub) Mathematical Association of America

Swarztrauber P N (1986) Symmetric FFT's Math. Comput. 47(175) 323–346

Wynn P (1956) On a device for computing the

e_{m} (S_{n})

transformation Math. Tables Aids Comput. 10 91–96

Is the data one-dimensional?			Multiple vectors?			c06psc
		yes			yes
	no			no
			c06pcc


Is the data two-dimensional?			c06puc
		yes
	no
Is the data three-dimensional?			c06pxc
		yes
	no
Transform on one dimension only?			c06pfc
		yes
	no
Transform on all dimensions?			c06pjc
		yes

Quarter-wave sine (inverse) transform?			c06rgc
		yes
	no
Quarter-wave cosine (inverse) transform?			c06rhc
		yes
	no
Sine (inverse) transform?			c06rec
		yes
	no
Cosine (inverse) transform?			c06rfc
		yes
	no
Is the data three-dimensional?			Forward transform on real data?			c06pyc
		yes			yes
	no			no
			Inverse transform on Hermitian data?			c06pzc
					yes

Is the data two-dimensional?			Forward transform on real data?			c06pvc
		yes			yes
	no			no
			Inverse transform on Hermitian data?			c06pwc
					yes

Is the data multi one-dimensional?			Sequence elements stored by stride (number of sequences)?			c06ppc
		yes			yes
	no			no
			Sequence elements stored contiguously?			c06pqc
					yes

c06pac

Function	Status	Replacement Function(s)
c06eac	Withdrawn at Mark 26	c06pac
c06ebc	Withdrawn at Mark 26	c06pac
c06ecc	Withdrawn at Mark 26	c06pcc
c06ekc	Withdrawn at Mark 26	c06fkc
c06fpc	To be withdrawn at Mark 30.2	c06pqc
c06fqc	To be withdrawn at Mark 30.2	c06pqc
c06frc	Withdrawn at Mark 26	c06psc
c06fuc	Withdrawn at Mark 26	c06puc
c06gbc	Withdrawn at Mark 26	No replacement required
c06gcc	Withdrawn at Mark 26	No replacement required
c06gqc	To be withdrawn at Mark 30.2	No replacement required
c06gsc	To be withdrawn at Mark 30.2	No replacement required
c06gzc	To be withdrawn at Mark 30.2	No replacement required
c06hac	Withdrawn at Mark 26	c06rec
c06hbc	Withdrawn at Mark 26	c06rfc
c06hcc	Withdrawn at Mark 26	c06rgc
c06hdc	Withdrawn at Mark 26	c06rhc

NAG CL Interface
C06 (Sum)
Fourier Transforms

▸▿ Contents

1 Scope of the Chapter

2 Background to the Problems

2.1 Discrete Fourier Transforms

2.1.1 Complex transforms

2.1.2 Real transforms

2.1.3 Real symmetric transforms

2.1.4 Fourier integral transforms

2.1.5 Convolutions and correlations

2.1.6 Applications to solving partial differential equations (PDEs)

2.2 Fast Gauss Transform

2.3 Direct Summation of Orthogonal Series

3 Recommendations on Choice and Use of Available Functions

3.1 One-dimensional Fourier Transforms

3.1.1 Real and Hermitian data

3.1.2 Complex data

3.2 Half- and Quarter-wave Transforms

3.3 Application to Elliptic Partial Differential Equations

3.4 Multidimensional Fourier Transforms

3.5 Convolution and Correlation

3.6 Fast Gauss Transform

3.7 Direct Summation of Orthogonal Series

4 Decision Trees

Tree 1: Fourier Transform of Discrete Complex Data

Tree 2: Fourier Transform of Real Data or Data in Complex Hermitian Form Resulting from the Transform of Real Data

5 Functionality Index

6 Auxiliary Functions Associated with Library Function Arguments

7 Withdrawn or Deprecated Functions

8 References

NAG CL InterfaceC06 (Sum)Fourier Transforms

▸▿ Contents

1 Scope of the Chapter

2 Background to the Problems

2.1 Discrete Fourier Transforms

2.1.1 Complex transforms

2.1.2 Real transforms

2.1.3 Real symmetric transforms

2.1.4 Fourier integral transforms

2.1.5 Convolutions and correlations

2.1.6 Applications to solving partial differential equations (PDEs)

2.2 Fast Gauss Transform

2.3 Direct Summation of Orthogonal Series

3 Recommendations on Choice and Use of Available Functions

3.1 One-dimensional Fourier Transforms

3.1.1 Real and Hermitian data

3.1.2 Complex data

3.2 Half- and Quarter-wave Transforms

3.3 Application to Elliptic Partial Differential Equations

3.4 Multidimensional Fourier Transforms

3.5 Convolution and Correlation

3.6 Fast Gauss Transform

3.7 Direct Summation of Orthogonal Series

4 Decision Trees

Tree 1: Fourier Transform of Discrete Complex Data

Tree 2: Fourier Transform of Real Data or Data in Complex Hermitian Form Resulting from the Transform of Real Data

5 Functionality Index

6 Auxiliary Functions Associated with Library Function Arguments

7 Withdrawn or Deprecated Functions

8 References

NAG CL Interface
C06 (Sum)
Fourier Transforms