g05zsf produces realizations of a stationary Gaussian random field in two dimensions, using the circulant embedding method. The square roots of the eigenvalues of the extended covariance matrix (or embedding matrix) need to be input, and can be calculated using g05zqf or g05zrf.

2 Specification

Fortran Interface

Subroutine g05zsf (

ns, s, m, lam, rho, state, z, ifail)

Integer, Intent (In)	::	ns(2), s, m(2)
Integer, Intent (Inout)	::	state(*), ifail
Real (Kind=nag_wp), Intent (In)	::	lam(M(1)*M(2)), rho
Real (Kind=nag_wp), Intent (Out)	::	z(NS(1)*NS(2),s)

C Header Interface

#include <nag.h>

void	g05zsf_ (const Integer ns[], const Integer s, const Integer m[], const double lam[], const double rho, Integer state[], double z[], Integer *ifail)

The routine may be called by the names g05zsf or nagf_rand_field_2d_generate.

3 Description

A two-dimensional random field

Z (x)

ℝ^{2}

is a function which is random at every point

x \in ℝ^{2}

, so

Z (x)

is a random variable for each

x

. The random field has a mean function

μ (x) = 𝔼 [Z (x)]

and a symmetric positive semidefinite covariance function

C (x, y) = 𝔼 [(Z (x) - μ (x)) (Z (y) - μ (y))]

Z (x)

is a Gaussian random field if for any choice of

n \in ℕ

and

x_{1}, \dots, x_{n} \in ℝ^{2}

, the random vector

{[Z (x_{1}), \dots, Z (x_{n})]}^{T}

follows a multivariate Normal distribution, which would have a mean vector

\tilde{μ}

with entries

{\tilde{μ}}_{i} = μ (x_{i})

and a covariance matrix

\tilde{C}

with entries

{\tilde{C}}_{i j} = C (x_{i}, x_{j})

. A Gaussian random field

Z (x)

is stationary if

μ (x)

is constant for all

x \in ℝ^{2}

and

C (x, y) = C (x + a, y + a)

for all

x, y, a \in ℝ^{2}

and hence we can express the covariance function

C (x, y)

as a function

γ

of one variable:

C (x, y) = γ (x - y)

γ

is known as a variogram (or more correctly, a semivariogram) and includes the multiplicative factor

σ^{2}

representing the variance such that

γ (0) = σ^{2}

The routines g05zqf or g05zrf along with g05zsf are used to simulate a two-dimensional stationary Gaussian random field, with mean function zero and variogram

γ (x)

, over a domain

[x_{\min}, x_{\max}] \times [y_{\min}, y_{\max}]

, using an equally spaced set of

N_{1} \times N_{2}

points;

N_{1}

points in the

x

-direction and

N_{2}

points in the

y

-direction. The problem reduces to sampling a Gaussian random vector

X

of size

N_{1} \times N_{2}

, with mean vector zero and a symmetric covariance matrix

A

, which is an

N_{2}

N_{2}

block Toeplitz matrix with Toeplitz blocks of size

N_{1}

N_{1}

. Since

A

is in general expensive to factorize, a technique known as the circulant embedding method is used.

A

is embedded into a larger, symmetric matrix

B

, which is an

M_{2}

M_{2}

block circulant matrix with circulant bocks of size

M_{1}

M_{1}

, where

M_{1} \geq 2 (N_{1} - 1)

and

M_{2} \geq 2 (N_{2} - 1)

B

can now be factorized as

B = W Λ W^{*} = R^{*} R

, where

W

is the two-dimensional Fourier matrix (

W^{*}

is the complex conjugate of

W

Λ

is the diagonal matrix containing the eigenvalues of

B

and

R = Λ^{\frac{1}{2}} W^{*}

B

is known as the embedding matrix. The eigenvalues can be calculated by performing a discrete Fourier transform of the first row (or column) of

B

and multiplying by

M_{1} \times M_{2}

, and so only the first row (or column) of

B

is needed – the whole matrix does not need to be formed.

The symmetry of

A

as a block matrix, and the symmetry of each block of

A

, depends on whether the covariance function

γ

is even or not.

γ

is even if

γ (x) = γ (- x)

for all

x \in ℝ^{2}

, and uneven otherwise (in higher dimensions,

γ

can be even in some coordinates and uneven in others, but in two dimensions

γ

is either even in both coordinates or uneven in both coordinates). If

γ

is even then

A

is a symmetric block matrix and has symmetric blocks; if

γ

is uneven then

A

is not a symmetric block matrix and has non-symmetric blocks. In the uneven case,

M_{1}

and

M_{2}

are set to be odd in order to guarantee symmetry in

B

As long as all of the values of

Λ

are non-negative (i.e.,

B

is positive semidefinite),

B

is a covariance matrix for a random vector

Y

which has

M_{2}

‘blocks’ of size

M_{1}

. Two samples of

Y

can now be simulated from the real and imaginary parts of

R^{*} (U + i V)

, where

U

and

V

have elements from the standard Normal distribution. Since

R^{*} (U + i V) = W Λ^{\frac{1}{2}} (U + i V)

, this calculation can be done using a discrete Fourier transform of the vector

Λ^{\frac{1}{2}} (U + i V)

. Two samples of the random vector

X

can now be recovered by taking the first

N_{1}

elements of the first

N_{2}

blocks of each sample of

Y

– because the original covariance matrix

A

is embedded in

B

X

will have the correct distribution.

B

is not positive semidefinite, larger embedding matrices

B

can be tried; however if the size of the matrix would have to be larger than maxm, an approximation procedure is used. See the documentation of g05zqf or g05zrf for details of the approximation procedure.

g05zsf takes the square roots of the eigenvalues of the embedding matrix

B

, and its size vector

M

, as input and outputs

S

realizations of the random field in

Z

One of the initialization routines g05kff (for a repeatable sequence if computed sequentially) or g05kgf (for a non-repeatable sequence) must be called prior to the first call to g05zsf.

4 References

Dietrich C R and Newsam G N (1997) Fast and exact simulation of stationary Gaussian processes through circulant embedding of the covariance matrix SIAM J. Sci. Comput. 18 1088–1107

Schlather M (1999) Introduction to positive definite functions and to unconditional simulation of random fields Technical Report ST 99–10 Lancaster University

Wood A T A and Chan G (1994) Simulation of stationary Gaussian processes in

{[0, 1]}^{d}

Journal of Computational and Graphical Statistics 3(4) 409–432

5 Arguments

1: $ns (2)$ – Integer array Input

On entry: the number of sample points to use in each direction, with

ns (1)

sample points in the

x

-direction and

ns (2)

sample points in the

y

-direction. The total number of sample points on the grid is therefore

ns (1) \times ns (2)

. This must be the same value as supplied to g05zqf or g05zrf when calculating the eigenvalues of the embedding matrix.

Constraints:

$ns (1) \geq 1$ ;
$ns (2) \geq 1$ .

2: $s$ – Integer Input

On entry:

S

, the number of realizations of the random field to simulate.

Constraint:

s \geq 1

3: $m (2)$ – Integer array Input

On entry: indicates the size,

M

, of the embedding matrix as returned by g05zqf or g05zrf. The embedding matrix is a block circulant matrix with circulant blocks.

m (1)

is the size of each block, and

m (2)

is the number of blocks.

Constraints:

$m (1) \geq \max (1, 2 (ns (1) - 1))$ ;
$m (2) \geq \max (1, 2 (ns (2) - 1))$ .

4: $lam (m (1) \times m (2))$ – Real (Kind=nag_wp) array Input

On entry: contains the square roots of the eigenvalues of the embedding matrix, as returned by g05zqf or g05zrf.

Constraint:

lam (i) \geq 0

, for

i = 1, 2, \dots, m (1) \times m (2)

5: $rho$ – Real (Kind=nag_wp) Input

On entry: indicates the scaling of the covariance matrix, as returned by g05zqf or g05zrf.

Constraint:

0.0 < rho \leq 1.0

6: $state (*)$ – Integer array Communication Array

Note: the actual argument supplied must be the array state supplied to the initialization routines g05kff or g05kgf.

On entry: contains information on the selected base generator and its current state.

On exit: contains updated information on the state of the generator.

7: $z (ns (1) \times ns (2), s)$ – Real (Kind=nag_wp) array Output

On exit: contains the realizations of the random field. The

k

th realization (where

k = 1, 2, \dots, s

) of the random field on the two-dimensional grid

(x_{i}, y_{j})

is stored in

z ((j - 1) \times ns (1) + i, k)

, for

i = 1, 2, \dots, ns (1)

and for

j = 1, 2, \dots, ns (2)

. The points are returned in xx and yy by g05zqf or g05zrf.

8: $ifail$ – Integer Input/Output

On entry: ifail must be set to

0

- 1 or 1

. If you are unfamiliar with this argument you should refer to Section 4 in the Introduction to the NAG Library FL Interface for details.

For environments where it might be inappropriate to halt program execution when an error is detected, the value

- 1 or 1

is recommended. If the output of error messages is undesirable, then the value

1

is recommended. Otherwise, if you are not familiar with this argument, the recommended value is

0

. When the value $- 1 or 1$ is used it is essential to test the value of ifail on exit.

On exit:

ifail = 0

unless the routine detects an error or a warning has been flagged (see Section 6).

6 Error Indicators and Warnings

If on entry

ifail = 0

- 1

, explanatory error messages are output on the current error message unit (as defined by x04aaf).

Errors or warnings detected by the routine:

$ifail = 1$: On entry, $ns = [〈value〉, 〈value〉]$ .
Constraint: $ns (1) \geq 1$ , $ns (2) \geq 1$ .

$ifail = 2$: On entry, $s = 〈value〉$ .
Constraint: $s \geq 1$ .

$ifail = 3$: On entry, $m = [〈value〉, 〈value〉]$ , and $ns = [〈value〉, 〈value〉]$ .
Constraints: $m (i) \geq \max (1, 2 (ns (i)) - 1)$ , for $i = 1, 2$ .

$ifail = 4$: On entry, at least one element of lam was negative.
Constraint: all elements of lam must be non-negative.

$ifail = 5$: On entry, $rho = 〈value〉$ .
Constraint: $0.0 < rho \leq 1.0$ .

$ifail = 6$: On entry, state vector has been corrupted or not initialized.

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 7 in the Introduction to the NAG Library FL Interface for further information.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.

7 Accuracy

Not applicable.

8 Parallelism and Performance

g05zsf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

g05zsf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.

Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

Because samples are generated in pairs, calling this routine

k

times, with

s = s

, say, will generate a different sequence of numbers than calling the routine once with

s = k s

, unless

s

is even.

10 Example

This example calls g05zsf to generate

5

realizations of a two-dimensional random field on a

5

5

grid. This uses eigenvalues of the embedding covariance matrix for a symmetric stable variogram as calculated by g05zrf with

icov2 = 1

Interfaces: FL CL AD

NAG FL Interface Introduction

G05 (Rand) Chapter Contents

G05 (Rand) Chapter Introduction

g05zs: FL CL AD

NAG FL Interfaceg05zsf (field_​2d_​generate)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG FL Interface
g05zsf (field_2d_generate)