NAG FL Interface
e01tmf (dim5_scat_shep)
1
Purpose
e01tmf generates a fivedimensional interpolant to a set of scattered data points, using a modified Shepard method.
2
Specification
Fortran Interface
Integer, Intent (In) 
:: 
m, nw, nq 
Integer, Intent (Inout) 
:: 
ifail 
Integer, Intent (Out) 
:: 
iq(2*m+1) 
Real (Kind=nag_wp), Intent (In) 
:: 
x(5,m), f(m) 
Real (Kind=nag_wp), Intent (Out) 
:: 
rq(21*m+11) 

C Header Interface
#include <nag.h>
void 
e01tmf_ (const Integer *m, const double x[], const double f[], const Integer *nw, const Integer *nq, Integer iq[], double rq[], Integer *ifail) 

C++ Header Interface
#include <nag.h> extern "C" {
void 
e01tmf_ (const Integer &m, const double x[], const double f[], const Integer &nw, const Integer &nq, Integer iq[], double rq[], Integer &ifail) 
}

The routine may be called by the names e01tmf or nagf_interp_dim5_scat_shep.
3
Description
e01tmf constructs a smooth function $Q\left(\mathbf{x}\right)$, $\mathbf{x}\in {\mathbb{R}}^{5}$ which interpolates a set of $m$ scattered data points $\left({\mathbf{x}}_{r},{f}_{r}\right)$, for $r=1,2,\dots ,m$, using a modification of Shepard's method. The surface is continuous and has continuous first partial derivatives.
The basic Shepard method, which is a generalization of the twodimensional method described in
Shepard (1968), interpolates the input data with the weighted mean
where
${q}_{r}={f}_{r}$,
${w}_{r}\left(\mathbf{x}\right)=\frac{1}{{d}_{r}^{2}}$ and
${d}_{r}^{2}={{\Vert \mathbf{x}{\mathbf{x}}_{r}\Vert}_{2}}^{2}$.
The basic method is global in that the interpolated value at any point depends on all the data, but
e01tmf uses a modification (see
Franke and Nielson (1980) and
Renka (1988a)), whereby the method becomes local by adjusting each
${w}_{r}\left(\mathbf{x}\right)$ to be zero outside a hypersphere with centre
${\mathbf{x}}_{r}$ and some radius
${R}_{w}$. Also, to improve the performance of the basic method, each
${q}_{r}$ above is replaced by a function
${q}_{r}\left(\mathbf{x}\right)$, which is a quadratic fitted by weighted least squares to data local to
${\mathbf{x}}_{r}$ and forced to interpolate
$\left({\mathbf{x}}_{r},{f}_{r}\right)$. In this context, a point
$\mathbf{x}$ is defined to be local to another point if it lies within some distance
${R}_{q}$ of it.
The efficiency of
e01tmf is enhanced by using a cell method for nearest neighbour searching due to
Bentley and Friedman (1979) with a cell density of
$3$.
The radii
${R}_{w}$ and
${R}_{q}$ are chosen to be just large enough to include
${N}_{w}$ and
${N}_{q}$ data points, respectively, for usersupplied constants
${N}_{w}$ and
${N}_{q}$. Default values of these arguments are provided, and advice on alternatives is given in
Section 9.2.
e01tmf is derived from the new implementation of QSHEP3 described by
Renka (1988b). It uses the modification for fivedimensional interpolation described by
Berry and Minser (1999).
Values of the interpolant
$Q\left(\mathbf{x}\right)$ generated by
e01tmf, and its first partial derivatives, can subsequently be evaluated for points in the domain of the data by a call to
e01tnf.
4
References
Bentley J L and Friedman J H (1979) Data structures for range searching ACM Comput. Surv. 11 397–409
Berry M W, Minser K S (1999) Algorithm 798: highdimensional interpolation using the modified Shepard method ACM Trans. Math. Software 25 353–366
Franke R and Nielson G (1980) Smooth interpolation of large sets of scattered data Internat. J. Num. Methods Engrg. 15 1691–1704
Renka R J (1988a) Multivariate interpolation of large sets of scattered data ACM Trans. Math. Software 14 139–148
Renka R J (1988b) Algorithm 661: QSHEP3D: Quadratic Shepard method for trivariate interpolation of scattered data ACM Trans. Math. Software 14 151–152
Shepard D (1968) A twodimensional interpolation function for irregularly spaced data Proc. 23rd Nat. Conf. ACM 517–523 Brandon/Systems Press Inc., Princeton
5
Arguments

1:
$\mathbf{m}$ – Integer
Input

On entry:
$m$, the number of data points.
Note: on the basis of experimental results reported in
Berry and Minser (1999), it is recommended to use
${\mathbf{m}}\ge 4000$.
Constraint:
${\mathbf{m}}\ge 23$.

2:
$\mathbf{x}\left(5,{\mathbf{m}}\right)$ – Real (Kind=nag_wp) array
Input

On entry: ${\mathbf{x}}\left(1:5,\mathit{r}\right)$ must be set to the Cartesian coordinates of the data point ${\mathbf{x}}_{\mathit{r}}$, for $\mathit{r}=1,2,\dots ,m$.
Constraint:
these coordinates must be distinct, and must not all lie on the same fourdimensional hypersurface.

3:
$\mathbf{f}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) array
Input

On entry: ${\mathbf{f}}\left(\mathit{r}\right)$ must be set to the data value ${f}_{\mathit{r}}$, for $\mathit{r}=1,2,\dots ,m$.

4:
$\mathbf{nw}$ – Integer
Input

On entry: the number
${N}_{w}$ of data points that determines each radius of influence
${R}_{w}$, appearing in the definition of each of the weights
${w}_{\mathit{r}}$, for
$\mathit{r}=1,2,\dots ,m$ (see
Section 3). Note that
${R}_{w}$ is different for each weight. If
${\mathbf{nw}}\le 0$ the default value
${\mathbf{nw}}=\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(32,{\mathbf{m}}1\right)$ is used instead.
Constraint:
${\mathbf{nw}}\le \mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(50,{\mathbf{m}}1\right)$.

5:
$\mathbf{nq}$ – Integer
Input

On entry: the number
${N}_{q}$ of data points to be used in the least squares fit for coefficients defining the quadratic functions
${q}_{r}\left(\mathbf{x}\right)$ (see
Section 3). If
${\mathbf{nq}}\le 0$ the default value
${\mathbf{nq}}=\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(50,{\mathbf{m}}1\right)$ is used instead.
Constraint:
${\mathbf{nq}}\le 0$ or $20\le {\mathbf{nq}}\le \mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(70,{\mathbf{m}}1\right)$.

6:
$\mathbf{iq}\left(2\times {\mathbf{m}}+1\right)$ – Integer array
Output

On exit: integer data defining the interpolant $Q\left(\mathbf{x}\right)$.

7:
$\mathbf{rq}\left(21\times {\mathbf{m}}+11\right)$ – Real (Kind=nag_wp) array
Output

On exit: real data defining the interpolant $Q\left(\mathbf{x}\right)$.

8:
$\mathbf{ifail}$ – Integer
Input/Output

On entry:
ifail must be set to
$0$,
$1$ or
$1$ to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of $0$ causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of $1$ means that an error message is printed while a value of $1$ means that it is not.
If halting is not appropriate, the value
$1$ or
$1$ is recommended. If message printing is undesirable, then the value
$1$ is recommended. Otherwise, the value
$0$ is recommended.
When the value $\mathbf{1}$ or $\mathbf{1}$ is used it is essential to test the value of ifail on exit.
On exit:
${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see
Section 6).
6
Error Indicators and Warnings
If on entry
${\mathbf{ifail}}=0$ or
$1$, explanatory error messages are output on the current error message unit (as defined by
x04aaf).
Errors or warnings detected by the routine:
 ${\mathbf{ifail}}=1$

On entry, ${\mathbf{m}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{m}}\ge 23$.
On entry, ${\mathbf{nq}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{nq}}\le 0$ or
${\mathbf{nq}}\ge 20$.
On entry, ${\mathbf{nq}}=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{m}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{nq}}\le \mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(70,{\mathbf{m}}1\right)$.
On entry, ${\mathbf{nw}}=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{m}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{nw}}\le \mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(50,{\mathbf{m}}1\right)$.
 ${\mathbf{ifail}}=2$

There are duplicate nodes in the dataset. ${\mathbf{x}}\left(i,k\right)={\mathbf{x}}\left(j,k\right)$, for $i=\u2329\mathit{\text{value}}\u232a$, $j=\u2329\mathit{\text{value}}\u232a$ and $k=1,2,\dots ,5$. The interpolant cannot be derived.
 ${\mathbf{ifail}}=3$

On entry, all the data points lie on the same fourdimensional hypersurface.
No unique solution exists.
 ${\mathbf{ifail}}=99$
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.
 ${\mathbf{ifail}}=399$
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.
 ${\mathbf{ifail}}=999$
Dynamic memory allocation failed.
See
Section 9 in the Introduction to the NAG Library FL Interface for further information.
7
Accuracy
On successful exit, the routine generated interpolates the input data exactly and has quadratic precision. Overall accuracy of the interpolant is affected by the choice of arguments
nw and
nq as well as the smoothness of the routine represented by the input data.
Berry and Minser (1999) report on the results obtained for a set of test routines.
8
Parallelism and Performance
e01tmf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
e01tmf 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 implementationspecific information.
The time taken for a call to e01tmf will depend in general on the distribution of the data points and on the choice of ${N}_{w}$ and ${N}_{q}$ parameters. If the data points are uniformly randomly distributed, then the time taken should be $\mathit{O}\left(m\right)$. At worst $\mathit{O}\left({m}^{2}\right)$ time will be required.
Default values of the arguments ${N}_{w}$ and ${N}_{q}$ may be selected by calling e01tmf with ${\mathbf{nw}}\le 0$ and ${\mathbf{nq}}\le 0$. These default values may well be satisfactory for many applications.
If nondefault values are required they must be supplied to
e01tmf through positive values of
nw and
nq. Increasing these argument values makes the method less local. This may increase the accuracy of the resulting interpolant at the expense of increased computational cost. The default values
${\mathbf{nw}}=\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(32,{\mathbf{m}}1\right)$ and
${\mathbf{nq}}=\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(50,{\mathbf{m}}1\right)$ have been chosen on the basis of experimental results reported in
Berry and Minser (1999). In these experiments the error norm was found to increase with the decrease of
${N}_{q}$, but to be little affected by the choice of
${N}_{w}$. The choice of both, directly affected the time taken by the routine. For further advice on the choice of these arguments see
Berry and Minser (1999).
9.3
Internal Changes
Internal changes have been made to this routine as follows:
 At Mark 26.0: The algorithm used by this routine, based on a Modified Shepard method, has been changed to produce more reliable results for some data sets which were previously not well handled. In addition, handling of evaluation points which are far away from the original data points has been improved by use of an extrapolation method which returns useful results rather than just an error message as was done at earlier Marks.
 At Mark 26.1: The algorithm has undergone further changes which enable it to work better on certain data sets, for example data presented on a regular grid. The results returned when evaluating the function at points which are not in the original data set used to construct the interpolating function are now likely to be slightly different from those returned at previous Marks of the Library, but the function still interpolates the original data.
For details of all known issues which have been reported for the NAG Library please refer to the
Known Issues.
10
Example
This program reads in a set of
$30$ data points and calls
e01tmf to construct an interpolating function
$Q\left(\mathbf{x}\right)$. It then calls
e01tnf to evaluate the interpolant at a set of points.
Note that this example is not typical of a realistic problem: the number of data points would normally be larger.
See also
Section 10 in
e01tnf.
10.1
Program Text
10.2
Program Data
10.3
Program Results