NAG Library Routine Document

E01SGF

The basic method is global in that the interpolated value at any point depends on all the data, but this routine uses a modification (see Franke and Nielson (1980) and Renka (1988a)), whereby the method becomes local by adjusting each

w_{r} (x, y)

to be zero outside a circle with centre

(x_{r}, y_{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} (x, y)

, which is a quadratic fitted by weighted least squares to data local to

(x_{r}, y_{r})

and forced to interpolate

(x_{r}, y_{r}, f_{r})

. In this context, a point

(x, y)

is defined to be local to another point if it lies within some distance

R_{q}

of it. Computation of these quadratics constitutes the main work done by this routine.

The efficiency of the routine is further enhanced by using a cell method for nearest neighbour searching due to Bentley and Friedman (1979).

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 user-supplied constants

N_{w}

and

N_{q}

. Default values of these parameters are provided by the routine, and advice on alternatives is given in Section 9.2.

This routine is derived from the routine QSHEP2 described by Renka (1988b).

Values of the interpolant

Q (x, y)

generated by this routine, and its first partial derivatives, can subsequently be evaluated for points in the domain of the data by a call to E01SHF.

4 References

Bentley J L and Friedman J H (1979) Data structures for range searching ACM Comput. Surv. 11 397–409

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 660: QSHEP2D: Quadratic Shepard method for bivariate interpolation of scattered data ACM Trans. Math. Software 14 149–150

Shepard D (1968) A two-dimensional interpolation function for irregularly spaced data Proc. 23rd Nat. Conf. ACM 517–523 Brandon/Systems Press Inc., Princeton

5 Parameters

1: $M$ – INTEGERInput: On entry: $m$ , the number of data points.

Constraint: $M \geq 6$ .
2: $X (M)$ – REAL (KIND=nag_wp) arrayInput
3: $Y (M)$ – REAL (KIND=nag_wp) arrayInput: On entry: the Cartesian coordinates of the data points $(x_{r}, y_{r})$ , for $r = 1, 2, \dots, m$ .

Constraint: these coordinates must be distinct, and must not all be collinear.
4: $F (M)$ – REAL (KIND=nag_wp) arrayInput: On entry: $F (r)$ must be set to the data value $f_{r}$ , for $r = 1, 2, \dots, m$ .
5: $NW$ – INTEGERInput: 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 $w_{r}$ , for $r = 1, 2, \dots, m$ (see Section 3). Note that $R_{w}$ is different for each weight. If $NW \leq 0$ the default value $NW = \min (19, M - 1)$ is used instead.

Constraint: $NW \leq \min (40, M - 1)$ .
6: $NQ$ – INTEGERInput: On entry: the number $N_{q}$ of data points to be used in the least squares fit for coefficients defining the nodal functions $q_{r} (x, y)$ (see Section 3). If $NQ \leq 0$ the default value $NQ = \min (13, M - 1)$ is used instead.

Constraint: $NQ \leq 0$ or $5 \leq NQ \leq \min (40, M - 1)$ .
7: $IQ (LIQ)$ – INTEGER arrayOutput: On exit: integer data defining the interpolant $Q (x, y)$ .
8: $LIQ$ – INTEGERInput: On entry: the dimension of the array IQ as declared in the (sub)program from which E01SGF is called.

Constraint: $LIQ \geq 2 \times M + 1$ .
9: $RQ (LRQ)$ – REAL (KIND=nag_wp) arrayOutput: On exit: real data defining the interpolant $Q (x, y)$ .
10: $LRQ$ – INTEGERInput: On entry: the dimension of the array RQ as declared in the (sub)program from which E01SGF is called.

Constraint: $LRQ \geq 6 \times M + 5$ .
11: $IFAIL$ – INTEGERInput/Output: On entry: IFAIL must be set to $0$ , $- 1 or 1$ . If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction 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 parameter, 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,	$M < 6$ ,
or	$0 < NQ < 5$ ,
or	$NQ > \min (40, M - 1)$ ,
or	$NW > \min (40, M - 1)$ ,
or	$LIQ < 2 \times M + 1$ ,
or	$LRQ < 6 \times M + 5$ .

$IFAIL = 2$

On entry,

(X (i), Y (i)) = (X (j), Y (j))

for some

i \neq j

$IFAIL = 3$

On entry,

all the data points are collinear. No unique solution exists.

$IFAIL = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.8 in the Essential Introduction for further information.

$IFAIL = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 3.7 in the Essential Introduction for further information.

$IFAIL = - 999$: Dynamic memory allocation failed.
See Section 3.6 in the Essential Introduction for further information.

7 Accuracy

On successful exit, the function generated interpolates the input data exactly and has quadratic accuracy.

8 Parallelism and Performance

E01SGF is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

E01SGF 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

9.1 Timing

The time taken for a call to E01SGF will depend in general on the distribution of the data points. If X and Y are uniformly randomly distributed, then the time taken should be

O (M)

. At worst

O (M^{2})

time will be required.

9.2 Choice of $N_{w}$ and $N_{q}$

Default values of the parameters

N_{w}

and

N_{q}

may be selected by calling E01SGF with

NW \leq 0

and

NQ \leq 0

. These default values may well be satisfactory for many applications.

If non-default values are required they must be supplied to E01SGF through positive values of NW and NQ. Increasing these parameters makes the method less local. This may increase the accuracy of the resulting interpolant at the expense of increased computational cost. The default values

NW = \min (19, M - 1)

and

NQ = \min (13, M - 1)

have been chosen on the basis of experimental results reported in Renka (1988a). In these experiments the error norm was found to vary smoothly with

N_{w}

and

N_{q}

, generally increasing monotonically and slowly with distance from the optimal pair. The method is not therefore thought to be particularly sensitive to the parameter values. For further advice on the choice of these parameters see Renka (1988a).

10 Example

This program reads in a set of

30

data points and calls E01SGF to construct an interpolating function

Q (x, y)

. It then calls E01SHF 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.

NAG Library Routine DocumentE01SGF

▸▿ Contents

1 Purpose

2 Specification

3 Description