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Chapter Contents
Chapter Introduction
NAG Toolbox

# NAG Toolbox: nag_rand_field_1d_predef_setup (g05zn)

## Purpose

nag_rand_field_1d_predef_setup (g05zn) performs the setup required in order to simulate stationary Gaussian random fields in one dimension, for a preset variogram, using the circulant embedding method. Specifically, the eigenvalues of the extended covariance matrix (or embedding matrix) are calculated, and their square roots output, for use by nag_rand_field_1d_generate (g05zp), which simulates the random field.

## Syntax

[lam, xx, m, approx, rho, icount, eig, ifail] = g05zn(ns, xmin, xmax, var, icov1, params, 'maxm', maxm, 'np', np, 'pad', pad, 'icorr', icorr)
[lam, xx, m, approx, rho, icount, eig, ifail] = nag_rand_field_1d_predef_setup(ns, xmin, xmax, var, icov1, params, 'maxm', maxm, 'np', np, 'pad', pad, 'icorr', icorr)

## Description

A one-dimensional random field Z(x)$Z\left(x\right)$ in $ℝ$ is a function which is random at every point x$x\in ℝ$, so Z(x)$Z\left(x\right)$ is a random variable for each x$x$. The random field has a mean function μ(x) = 𝔼[Z(x)]$\mu \left(x\right)=𝔼\left[Z\left(x\right)\right]$ and a symmetric positive semidefinite covariance function C(x,y) = 𝔼[(Z(x)μ(x))(Z(y)μ(y))]$C\left(x,y\right)=𝔼\left[\left(Z\left(x\right)-\mu \left(x\right)\right)\left(Z\left(y\right)-\mu \left(y\right)\right)\right]$. Z(x)$Z\left(x\right)$ is a Gaussian random field if for any choice of n$n\in ℕ$ and x1,,xn${x}_{1},\dots ,{x}_{n}\in ℝ$, the random vector [Z(x1),,Z(xn)]T${\left[Z\left({x}_{1}\right),\dots ,Z\left({x}_{n}\right)\right]}^{\mathrm{T}}$ follows a multivariate Gaussian distribution, which would have a mean vector μ̃$\stackrel{~}{\mathbf{\mu }}$ with entries μ̃i = μ(xi)${\stackrel{~}{\mu }}_{i}=\mu \left({x}_{i}\right)$ and a covariance matrix $\stackrel{~}{C}$ with entries ij = C(xi,xj)${\stackrel{~}{C}}_{ij}=C\left({x}_{i},{x}_{j}\right)$. A Gaussian random field Z(x)$Z\left(x\right)$ is stationary if μ(x)$\mu \left(x\right)$ is constant for all x$x\in ℝ$ and C(x,y) = C(x + a,y + a)$C\left(x,y\right)=C\left(x+a,y+a\right)$ for all x,y,a$x,y,a\in ℝ$ and hence we can express the covariance function C(x,y)$C\left(x,y\right)$ as a function γ$\gamma$ of one variable: C(x,y) = γ(xy)$C\left(x,y\right)=\gamma \left(x-y\right)$. γ$\gamma$ is known as a variogram (or more correctly, a semivariogram) and includes the multiplicative factor σ2${\sigma }^{2}$ representing the variance such that γ(0) = σ2$\gamma \left(0\right)={\sigma }^{2}$.
The functions nag_rand_field_1d_predef_setup (g05zn) and nag_rand_field_1d_generate (g05zp) are used to simulate a one-dimensional stationary Gaussian random field, with mean function zero and variogram γ(x)$\gamma \left(x\right)$, over an interval [xmin,xmax]$\left[{x}_{\mathrm{min}},{x}_{\mathrm{max}}\right]$, using an equally spaced set of N$N$ points. The problem reduces to sampling a Gaussian random vector X$\mathbf{X}$ of size N$N$, with mean vector zero and a symmetric Toeplitz covariance matrix A$A$. Since A$A$ is in general expensive to factorize, a technique known as the circulant embedding method is used. A$A$ is embedded into a larger, symmetric circulant matrix B$B$ of size M2(N1)$M\ge 2\left(N-1\right)$, which can now be factorized as B = WΛW* = R*R$B=W\Lambda {W}^{*}={R}^{*}R$, where W$W$ is the Fourier matrix (W*${W}^{*}$ is the complex conjugate of W$W$), Λ$\Lambda$ is the diagonal matrix containing the eigenvalues of B$B$ and R = Λ(1/2)W*$R={\Lambda }^{\frac{1}{2}}{W}^{*}$. B$B$ 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$B$ and multiplying by M$M$, and so only the first row (or column) of B$B$ is needed – the whole matrix does not need to be formed.
As long as all of the values of Λ$\Lambda$ are non-negative (i.e., B$B$ is positive semidefinite), B$B$ is a covariance matrix for a random vector Y$\mathbf{Y}$, two samples of which can now be simulated from the real and imaginary parts of R*(U + iV)${R}^{*}\left(\mathbf{U}+i\mathbf{V}\right)$, where U$\mathbf{U}$ and V$\mathbf{V}$ have elements from the standard Normal distribution. Since R*(U + iV) = WΛ(1/2)(U + iV)${R}^{*}\left(\mathbf{U}+i\mathbf{V}\right)=W{\Lambda }^{\frac{1}{2}}\left(\mathbf{U}+i\mathbf{V}\right)$, this calculation can be done using a discrete Fourier transform of the vector Λ(1/2)(U + iV)${\Lambda }^{\frac{1}{2}}\left(\mathbf{U}+i\mathbf{V}\right)$. Two samples of the random vector X$\mathbf{X}$ can now be recovered by taking the first N$N$ elements of each sample of Y$\mathbf{Y}$ – because the original covariance matrix A$A$ is embedded in B$B$, X$\mathbf{X}$ will have the correct distribution.
If B$B$ is not positive semidefinite, larger embedding matrices B$B$ can be tried; however if the size of the matrix would have to be larger than maxm, an approximation procedure is used. We write Λ = Λ+ + Λ$\Lambda ={\Lambda }_{+}+{\Lambda }_{-}$, where Λ+${\Lambda }_{+}$ and Λ${\Lambda }_{-}$ contain the non-negative and negative eigenvalues of B$B$ respectively. Then B$B$ is replaced by ρB+$\rho {B}_{+}$ where B+ = WΛ+W*${B}_{+}=W{\Lambda }_{+}{W}^{*}$ and ρ(0,1]$\rho \in \left(0,1\right]$ is a scaling factor. The error ε$\epsilon$ in approximating the distribution of the random field is given by
 ε = sqrt( ( (1 − ρ)2 traceΛ + ρ2 traceΛ− )/M ) . $ε= (1-ρ) 2 trace⁡Λ + ρ2 trace⁡Λ- M .$
Three choices for ρ$\rho$ are available, and are determined by the input parameter icorr:
• setting icorr = 0${\mathbf{icorr}}=0$ sets
 ρ = (traceΛ)/(traceΛ+) , $ρ= trace⁡Λ trace⁡Λ+ ,$
• setting icorr = 1${\mathbf{icorr}}=1$ sets
 ρ = sqrt( (traceΛ)/(traceΛ+) ) , $ρ= trace⁡Λ trace⁡Λ+ ,$
• setting icorr = 2${\mathbf{icorr}}=2$ sets ρ = 1$\rho =1$.
nag_rand_field_1d_predef_setup (g05zn) finds a suitable positive semidefinite embedding matrix B$B$ and outputs its size, m, and the square roots of its eigenvalues in lam. If approximation is used, information regarding the accuracy of the approximation is output. Note that only the first row (or column) of B$B$ is actually formed and stored.

## 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 (1997) Algorithm AS 312: An Algorithm for Simulating Stationary Gaussian Random Fields Journal of the Royal Statistical Society, Series C (Applied Statistics) (Volume 46) 1 171–181

## Parameters

### Compulsory Input Parameters

1:     ns – int64int32nag_int scalar
The number of sample points (points) to be generated in realisations of the random field.
Constraint: ns1${\mathbf{ns}}\ge 1$.
2:     xmin – double scalar
The lower bound for the interval over which the random field is to be simulated. Note that if icov1 is set to 14$14$ (for simulating fractional Brownian motion), xmin is not referenced and the lower bound for the interval is set to zero.
Constraint: if icov114${\mathbf{icov1}}\ne 14$, ${\mathbf{xmin}}<{\mathbf{xmax}}$.
3:     xmax – double scalar
The upper bound for the interval over which the random field is to be simulated. Note that if icov1 is set to 14$14$ (for simulating fractional Brownian motion), the lower bound for the interval is set to zero and so xmax is required to be greater than zero.
Constraints:
• if icov114${\mathbf{icov1}}\ne 14$, ${\mathbf{xmin}}<{\mathbf{xmax}}$;
• if icov1 = 14${\mathbf{icov1}}=14$, xmax > 0.0${\mathbf{xmax}}>0.0$.
4:     var – double scalar
The multiplicative factor σ2${\sigma }^{2}$ of the variogram γ(x)$\gamma \left(x\right)$.
Constraint: var0.0${\mathbf{var}}\ge 0.0$.
5:     icov1 – int64int32nag_int scalar
Determines which of the preset variograms to use. The choices are given below. Note that x = (|x|)/${x}^{\prime }=\frac{|x|}{\ell }$, where $\ell$ is the correlation length and is a parameter for most of the variograms, and σ2${\sigma }^{2}$ is the variance specified by var.
icov1 = 1${\mathbf{icov1}}=1$
Symmetric stable variogram
 γ(x) = σ2 exp( − (x′)ν) , $γ(x) = σ2 exp( - (x′) ν ) ,$
where
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$,
• ν = params(2)$\nu ={\mathbf{params}}\left(2\right)$, 0ν2$0\le \nu \le 2$.
icov1 = 2${\mathbf{icov1}}=2$
Cauchy variogram
 γ(x) = σ2 (1 + (x′)2) − ν , $γ(x) = σ2 ( 1+ (x′) 2 ) -ν ,$
where
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$,
• ν = params(2)$\nu ={\mathbf{params}}\left(2\right)$, ν > 0$\nu >0$.
icov1 = 3${\mathbf{icov1}}=3$
Differential variogram with compact support
γ(x) =
 { σ2(1 + 8x′ + 25(x′)2 + 32(x′)3)(1 − x′)8, x′ < 1, 0, x′ ≥ 1,
$γ(x) = { σ2(1+8x′+25(x′)2+32(x′)3)(1-x′)8, x′<1, 0, x′≥1,$
where
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$.
icov1 = 4${\mathbf{icov1}}=4$
Exponential variogram
 γ(x) = σ2exp( − x′), $γ(x)=σ2exp(-x′),$
where
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$.
icov1 = 5${\mathbf{icov1}}=5$
Gaussian variogram
 γ(x) = σ2exp( − (x′)2), $γ(x)=σ2exp(-(x′)2),$
where
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$.
icov1 = 6${\mathbf{icov1}}=6$
Nugget variogram
γ(x) =
 { σ2, x = 0, 0, x ≠ 0.
$γ(x)={ σ2, x=0, 0, x≠0.$
No parameters need be set for this value of icov1.
icov1 = 7${\mathbf{icov1}}=7$
Spherical variogram
γ(x) =
 { σ2(1 − 1.5x′ + 0.5(x′)3), x′ < 1, 0, x′ ≥ 1,
$γ(x)={ σ2(1-1.5x′+0.5(x′)3), x′<1, 0, x′≥1,$
where
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$.
icov1 = 8${\mathbf{icov1}}=8$
Bessel variogram
 γ(x) = σ2(2νΓ(ν + 1)Jν(x′))/((x′)ν), $γ(x)=σ22νΓ(ν+1)Jν(x′)(x′)ν,$
where
• Jν( · )${J}_{\nu }\left(·\right)$ is the Bessel function of the first kind,
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$,
• ν = params(2)$\nu ={\mathbf{params}}\left(2\right)$, ν0.5$\nu \ge -0.5$.
icov1 = 9${\mathbf{icov1}}=9$
Hole effect variogram
 γ(x) = σ2(sin(x′))/(x′), $γ(x)=σ2sin(x′)x′,$
where
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$.
icov1 = 10${\mathbf{icov1}}=10$
Whittle-Matérn variogram
 γ(x) = σ2(21 − ν(x′)νKν(x′))/(Γ(ν)), $γ(x)=σ221-ν(x′)νKν(x′)Γ(ν),$
where
• Kν( · )${K}_{\nu }\left(·\right)$ is the modified Bessel function of the second kind,
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$,
• ν = params(2)$\nu ={\mathbf{params}}\left(2\right)$, ν > 0$\nu >0$.
icov1 = 11${\mathbf{icov1}}=11$
Continuously parameterised variogram with compact support
γ(x) =
 { σ2(21 − ν(x′)νKν(x′))/(Γ(ν))(1 + 8x′′ + 25(x′′)2 + 32(x′′)3)(1 − x′′)8, x′′ < 1, 0, x′′ ≥ 1,
$γ(x)={ σ221-ν(x′)νKν(x′)Γ(ν)(1+8x′′+25(x′′)2+32(x′′)3)(1-x′′)8, x′′<1, 0, x′′≥1,$
where
• x = (x)/s ${x}^{\prime \prime }=\frac{{x}^{\prime }}{s}$,
• Kν( · )${K}_{\nu }\left(·\right)$ is the modified Bessel function of the second kind,
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$,
• s = params(2)$s={\mathbf{params}}\left(2\right)$, s > 0$s>0$ (second correlation length),
• ν = params(3)$\nu ={\mathbf{params}}\left(3\right)$, ν > 0$\nu >0$.
icov1 = 12${\mathbf{icov1}}=12$
Generalized hyperbolic distribution variogram
 γ(x) = σ2((δ2 + (x′)2)λ/2)/(δλKλ(κδ))Kλ(κ(δ2 + (x′)2)(1/2)), $γ(x)=σ2(δ2+(x′)2)λ2δλKλ(κδ)Kλ(κ(δ2+(x′)2)12),$
where
• Kλ( · )${K}_{\lambda }\left(·\right)$ is the modified Bessel function of the second kind,
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$,
• λ = params(2)$\lambda ={\mathbf{params}}\left(2\right)$, no constraint on λ$\lambda$
• δ = params(3)$\delta ={\mathbf{params}}\left(3\right)$, δ > 0$\delta >0$,
• κ = params(4)$\kappa ={\mathbf{params}}\left(4\right)$, κ > 0$\kappa >0$.
icov1 = 13${\mathbf{icov1}}=13$
Cosine variogram
 γ(x) = σ2cos(x′), $γ(x)=σ2cos(x′),$
where
• = params(1)$\ell ={\mathbf{params}}\left(1\right)$, > 0$\ell >0$.
icov1 = 14${\mathbf{icov1}}=14$
Used for simulating fractional Brownian motion BH(t)${B}^{H}\left(t\right)$. Fractional Brownian motion itself is not a stationary Gaussian random field, but its increments (i) = BH(ti)BH(ti1)$\stackrel{~}{X}\left(i\right)={B}^{H}\left({t}_{i}\right)-{B}^{H}\left({t}_{i-1}\right)$ can be simulated in the same way as a stationary random field. The variogram for the so-called ‘increment process’ is
 C(X̃(ti),X̃(tj)) = γ̃(x) = (δ2H)/2(|x/δ − 1|2H + |x/δ + 1|2H − 2|x/δ|2H), $C(X~(ti),X~(tj))=γ~(x)=δ2H2(|xδ-1|2H+|xδ+1|2H-2|xδ|2H),$
where
• x = tjti$x={t}_{j}-{t}_{i}$,
• H = params(1)$H={\mathbf{params}}\left(1\right)$, 0 < H < 1$0, H$H$ is the Hurst parameter,
• δ = params(2)$\delta ={\mathbf{params}}\left(2\right)$, δ > 0$\delta >0$, normally δ = titi1$\delta ={t}_{i}-{t}_{i-1}$ is the (fixed) stepsize.
We scale the increments to set γ(0) = 1$\gamma \left(0\right)=1$; let X(i) = ((i))/(δH)$X\left(i\right)=\frac{\stackrel{~}{X}\left(i\right)}{{\delta }^{-H}}$, then
 C(X(ti),X(tj)) = γ(x) = (1/2) (|x/δ − 1|2H + |x/δ + 1|2H − 2|x/δ|2H) . $C(X(ti),X(tj)) = γ(x) = 12 ( | xδ - 1 | 2H + | xδ + 1 | 2H - 2 | xδ | 2H ) .$
The increments X(i)$X\left(i\right)$ can then be simulated using nag_rand_field_1d_generate (g05zp), then multiplied by δH${\delta }^{H}$ to obtain the original increments (i)$\stackrel{~}{X}\left(i\right)$ for the fractional Brownian motion.
6:     params(np) – double array
The parameters set for the variogram.
Constraint: see icov1 for a description of the individual parameter constraints.

### Optional Input Parameters

1:     maxm – int64int32nag_int scalar
The maximum size of the circulant matrix to use. For example, if the embedding matrix is to be allowed to double in size three times before the approximation procedure is used, then choose maxm = 2k + 2 ${\mathbf{maxm}}={2}^{k+2}$ where k = 1 + log2(ns1) $k=1+⌈{\mathrm{log}}_{2}\left({\mathbf{ns}}-1\right)⌉$.
Default: 23 + ceilinglog2(ns1)${2}^{3+\mathrm{ceiling}{\mathrm{log}}_{2}\left({\mathbf{ns}}-1\right)}$
Constraint: maxm 2k ${\mathbf{maxm}}\ge {2}^{k}$, where k$k$ is the smallest integer satisfying 2k 2 (ns1) ${2}^{k}\ge 2\left({\mathbf{ns}}-1\right)$.
2:     np – int64int32nag_int scalar
Default: The dimension of the array params.
The number of parameters to be set. Different variograms need a different number of parameters.
icov1 = 6${\mathbf{icov1}}=6$
np must be set to 0$0$.
icov1 = 3${\mathbf{icov1}}=3$, 4$4$, 5$5$, 7$7$, 9$9$ or 13$13$
np must be set to 1$1$.
icov1 = 1${\mathbf{icov1}}=1$, 2$2$, 8$8$, 10$10$ or 14$14$
np must be set to 2$2$.
icov1 = 11${\mathbf{icov1}}=11$
np must be set to 3$3$.
icov1 = 12${\mathbf{icov1}}=12$
np must be set to 4$4$.
Determines whether the embedding matrix is padded with zeros, or padded with values of the variogram. The choice of padding may affect how big the embedding matrix must be in order to be positive semidefinite.
pad = 0${\mathbf{pad}}=0$
The embedding matrix is padded with zeros.
pad = 1${\mathbf{pad}}=1$
The embedding matrix is padded with values of the variogram.
Default: pad = 1${\mathbf{pad}}=1$
Constraint: pad = 0${\mathbf{pad}}=0$ or 1$1$.
4:     icorr – int64int32nag_int scalar
Determines which approximation to implement if required, as described in Section [Description].
Default: icorr = 0${\mathbf{icorr}}=0$
Constraint: icorr = 0${\mathbf{icorr}}=0$, 1$1$ or 2$2$.

None.

### Output Parameters

1:     lam(maxm) – double array
Contains the square roots of the eigenvalues of the embedding matrix.
2:     xx(ns) – double array
The points at which values of the random field will be output.
3:     m – int64int32nag_int scalar
The size of the embedding matrix.
4:     approx – int64int32nag_int scalar
Indicates whether approximation was used.
approx = 0${\mathbf{approx}}=0$
No approximation was used.
approx = 1${\mathbf{approx}}=1$
Approximation was used.
5:     rho – double scalar
Indicates the scaling of the covariance matrix. rho = 1.0${\mathbf{rho}}=1.0$ unless approximation was used with icorr = 0${\mathbf{icorr}}=0$ or 1$1$.
6:     icount – int64int32nag_int scalar
Indicates the number of negative eigenvalues in the embedding matrix which have had to be set to zero.
7:     eig(3$3$) – double array
Indicates information about the negative eigenvalues in the embedding matrix which have had to be set to zero. eig(1)${\mathbf{eig}}\left(1\right)$ contains the smallest eigenvalue, eig(2)${\mathbf{eig}}\left(2\right)$ contains the sum of the squares of the negative eigenvalues, and eig(3)${\mathbf{eig}}\left(3\right)$ contains the sum of the absolute values of the negative eigenvalues.
8:     ifail – int64int32nag_int scalar
${\mathrm{ifail}}={\mathbf{0}}$ unless the function detects an error (see [Error Indicators and Warnings]).

## Error Indicators and Warnings

Errors or warnings detected by the function:
ifail = 1${\mathbf{ifail}}=1$
Constraint: ns1${\mathbf{ns}}\ge 1$.
ifail = 2${\mathbf{ifail}}=2$
Constraint: ${\mathbf{xmin}}<{\mathbf{xmax}}$.
ifail = 3${\mathbf{ifail}}=3$
Constraint: xmax > 0.0${\mathbf{xmax}}>0.0$.
ifail = 4${\mathbf{ifail}}=4$
Constraint: the calculated minimum value for maxm is _$_$.
Where the minimum calculated value is given by 2k ${2}^{k}$, where k$k$ is the smallest integer satisfying 2k 2 (ns1) ${2}^{k}\ge 2\left({\mathbf{ns}}-1\right)$.
ifail = 5${\mathbf{ifail}}=5$
Constraint: var0.0${\mathbf{var}}\ge 0.0$.
ifail = 6${\mathbf{ifail}}=6$
Constraint: icov11${\mathbf{icov1}}\ge 1$ and icov114${\mathbf{icov1}}\le 14$.
ifail = 7${\mathbf{ifail}}=7$
Constraint: for icov1 = _${\mathbf{icov1}}=_$.
On entry, np is not the correct number of parameters for the specified variogram.
ifail = 8${\mathbf{ifail}}=8$
Constraint: dependent on icov1, see documentation.
ifail = 9${\mathbf{ifail}}=9$
Constraint: pad = 0${\mathbf{pad}}=0$ or 1$1$.
ifail = 10${\mathbf{ifail}}=10$
Constraint: icorr = 0${\mathbf{icorr}}=0$, 1$1$ or 2$2$.

Not applicable.

None.

## Example

```function nag_rand_field_1d_predef_setup_example
icov1 = int64(1);
params = [0.1; 1.2];
var = 0.5;
xmin = -1;
xmax = 1;
ns = int64(8);
icorr = int64(2);
% Get square roots of the eigenvalues of the embedding matrix
[lam, xx, m, approx, rho, icount, eig, ifail] = ...
nag_rand_field_1d_predef_setup(ns, xmin, xmax, var, icov1, ...
params, 'icorr', icorr);

fprintf('\nSize of embedding matrix = %d\n\n', m);

% Display approximation information if approximation used
if approx == 1
fprintf('Approximation required\n\n');
fprintf('rho = %10.5f\n', rho);
fprintf('eig = %10.5f%10.5f%10.5f\n', eig(1:3));
fprintf('icount = %d\n', icount);
else
fprintf('Approximation not required\n\n');
end

% Display square roots of the eigenvalues of the embedding matrix
fprintf('Square roots of eigenvalues of embedding matrix:\n');
disp(lam(1:m));
```
```

Size of embedding matrix = 16

Approximation not required

Square roots of eigenvalues of embedding matrix:
0.7421
0.7393
0.7315
0.7199
0.7064
0.6930
0.6818
0.6744
0.6718
0.6744
0.6818
0.6930
0.7064
0.7199
0.7315
0.7393

```
```function g05zn_example
icov1 = int64(1);
params = [0.1; 1.2];
var = 0.5;
xmin = -1;
xmax = 1;
ns = int64(8);
icorr = int64(2);
% Get square roots of the eigenvalues of the embedding matrix
[lam, xx, m, approx, rho, icount, eig, ifail] = ...
g05zn(ns, xmin, xmax, var, icov1, params, 'icorr', icorr);

fprintf('\nSize of embedding matrix = %d\n\n', m);

% Display approximation information if approximation used
if approx == 1
fprintf('Approximation required\n\n');
fprintf('rho = %10.5f\n', rho);
fprintf('eig = %10.5f%10.5f%10.5f\n', eig(1:3));
fprintf('icount = %d\n', icount);
else
fprintf('Approximation not required\n\n');
end

% Display square roots of the eigenvalues of the embedding matrix
fprintf('Square roots of eigenvalues of embedding matrix:\n');
disp(lam(1:m));
```
```

Size of embedding matrix = 16

Approximation not required

Square roots of eigenvalues of embedding matrix:
0.7421
0.7393
0.7315
0.7199
0.7064
0.6930
0.6818
0.6744
0.6718
0.6744
0.6818
0.6930
0.7064
0.7199
0.7315
0.7393

```