Integer, Intent (In)	::	mx, my, px, py, nux, nuy
Integer, Intent (Inout)	::	ifail
Real (Kind=nag_wp), Intent (In)	::	x(mx), y(my), lamda(px), mu(py), c((px-4)*(py-4))
Real (Kind=nag_wp), Intent (Out)	::	z(mx*my)

C Header Interface

#include <nag.h>

void	e02dhf_ (const Integer mx, const Integer my, const Integer px, const Integer py, const double x[], const double y[], const double lamda[], const double mu[], const double c[], const Integer nux, const Integer nuy, double z[], Integer *ifail)

The routine may be called by the names e02dhf or nagf_fit_dim2_spline_derivm.

3 Description

e02dhf determines the partial derivative

\frac{\partial^{ν_{x} + ν_{y}}}{\partial x^{ν_{x}} \partial y^{ν_{y}}}

of a smooth bicubic spline approximation

s (x, y)

at the set of data points

(x_{q}, y_{r})

The spline is given in the B-spline representation

s (x, y) = \sum_{i = 1}^{n_{x} - 4} \sum_{j = 1}^{n_{y} - 4} c_{i j} M_{i} (x) N_{j} (y),

(1)

where

M_{i} (x)

and

N_{j} (y)

denote normalized cubic B-splines, the former defined on the knots

λ_{i}

λ_{i + 4}

and the latter on the knots

μ_{j}

μ_{j + 4}

, with

n_{x}

and

n_{y}

the total numbers of knots of the computed spline with respect to the

x

and

y

variables respectively. For further details, see Hayes and Halliday (1974) for bicubic splines and de Boor (1972) for normalized B-splines. This routine is suitable for B-spline representations returned by e01daf, e02daf, e02dcf and e02ddf.

The partial derivatives can be up to order

2

in each direction; thus the highest mixed derivative available is

\frac{\partial^{4}}{\partial x^{2} \partial y^{2}}

The points in the grid are defined by coordinates

x_{q}

, for

q = 1, 2, \dots, m_{x}

, along the

x

axis, and coordinates

y_{r}

, for

r = 1, 2, \dots, m_{y}

, along the

y

axis.

4 References

de Boor C (1972) On calculating with B-splines J. Approx. Theory 6 50–62

Dierckx P (1981) An improved algorithm for curve fitting with spline functions Report TW54 Department of Computer Science, Katholieke Univerciteit Leuven

Dierckx P (1982) A fast algorithm for smoothing data on a rectangular grid while using spline functions SIAM J. Numer. Anal. 19 1286–1304

Hayes J G and Halliday J (1974) The least squares fitting of cubic spline surfaces to general data sets J. Inst. Math. Appl. 14 89–103

Reinsch C H (1967) Smoothing by spline functions Numer. Math. 10 177–183

5 Arguments

1: $mx$ – Integer Input: On entry: $m_{x}$ , the number of grid points along the $x$ axis.

Constraint: $mx \geq 1$ .
2: $my$ – Integer Input: On entry: $m_{y}$ , the number of grid points along the $y$ axis.

Constraint: $my \geq 1$ .
3: $px$ – Integer Input: On entry: the total number of knots in the $x$ -direction of the bicubic spline approximation, e.g., the value nx as returned by e02dcf.
4: $py$ – Integer Input: On entry: the total number of knots in the $y$ -direction of the bicubic spline approximation, e.g., the value ny as returned by e02dcf.
5: $x (mx)$ – Real (Kind=nag_wp) array Input: On entry: $x (q)$ must be set to $x_{q}$ , the $x$ coordinate of the $q$ th grid point along the $x$ axis, for $q = 1, 2, \dots, m_{x}$ , on which values of the partial derivative are sought.

Constraint: $x_{1} < x_{2} < \dots < x_{m_{x}}$ .
6: $y (my)$ – Real (Kind=nag_wp) array Input: On entry: $y (r)$ must be set to $y_{r}$ , the $y$ coordinate of the $r$ th grid point along the $y$ axis, for $r = 1, 2, \dots, m_{y}$ on which values of the partial derivative are sought.

Constraint: $y_{1} < y_{2} < \dots < y_{m_{y}}$ .
7: $lamda (px)$ – Real (Kind=nag_wp) array Input: On entry: contains the position of the knots in the $x$ -direction of the bicubic spline approximation to be differentiated, e.g., lamda as returned by e02dcf.
8: $mu (py)$ – Real (Kind=nag_wp) array Input: On entry: contains the position of the knots in the $y$ -direction of the bicubic spline approximation to be differentiated, e.g., mu as returned by e02dcf.
9: $c ((px - 4) \times (py - 4))$ – Real (Kind=nag_wp) array Input: On entry: the coefficients of the bicubic spline approximation to be differentiated, e.g., c as returned by e02dcf.
10: $nux$ – Integer Input: On entry: specifies the order, $ν_{x}$ of the partial derivative in the $x$ -direction.

Constraint: $0 \leq nux \leq 2$ .
11: $nuy$ – Integer Input: On entry: specifies the order, $ν_{y}$ of the partial derivative in the $y$ -direction.

Constraint: $0 \leq nuy \leq 2$ .
12: $z (mx \times my)$ – Real (Kind=nag_wp) array Output: On exit: $z (m_{y} \times (q - 1) + r)$ contains the derivative $\frac{\partial^{ν_{x} + ν_{y}}}{{\partial x}^{ν_{x}} {\partial y}^{ν_{y}}} s (x_{q}, y_{r})$ , for $q = 1, 2, \dots, m_{x}$ and $r = 1, 2, \dots, m_{y}$ .
13: $ifail$ – 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 $- 1$ 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, $nux = ⟨ value ⟩$ .
Constraint: $0 \leq nux \leq 2$ .

$ifail = 2$: On entry, $nuy = ⟨ value ⟩$ .
Constraint: $0 \leq nuy \leq 2$ .

$ifail = 3$: On entry, $mx = ⟨ value ⟩$ .
Constraint: $mx \geq 1$ .

$ifail = 4$: On entry, $my = ⟨ value ⟩$ .
Constraint: $my \geq 1$ .

$ifail = 5$: On entry, for $i = ⟨ value ⟩$ , $x (i - 1) = ⟨ value ⟩$ and $x (i) = ⟨ value ⟩$ .
Constraint: $x (i - 1) \leq x (i)$ , for $i = 2, 3, \dots, mx$ .

$ifail = 6$: On entry, for $i = ⟨ value ⟩$ , $y (i - 1) = ⟨ value ⟩$ and $y (i) = ⟨ value ⟩$ .
Constraint: $y (i - 1) \leq y (i)$ , for $i = 2, 3, \dots, my$ .

$ifail = - 99$: 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

On successful exit, the partial derivatives on the given mesh are accurate to machine precision with respect to the supplied bicubic spline. Please refer to Section 7 in e01daf, e02daf, e02dcf and e02ddf of the routine document for the respective routine which calculated the spline approximant for details on the accuracy of that approximation.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

e02dhf 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

None.

10 Example

This example reads in values of

m_{x}

m_{y}

x_{q}

, for

q = 1, 2, \dots, m_{x}

, and

y_{r}

, for

r = 1, 2, \dots, m_{y}

, followed by values of the ordinates

f_{q, r}

defined at the grid points

(x_{q}, y_{r})

. It then calls e02dcf to compute a bicubic spline approximation for one specified value of

S

. Finally it evaluates the spline and its first

x

derivative at a small sample of points on a rectangular grid by calling e02dhf.

e02dh: FL CL CPP AD PY MB

NAG FL Interfacee02dhf (dim2_​spline_​derivm)

▸▿ 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
e02dhf (dim2_spline_derivm)