NAG FL Interface
e02ddf (dim2_spline_sctr)
1
Purpose
e02ddf computes a bicubic spline approximation to a set of scattered data. The knots of the spline are located automatically, but a single argument must be specified to control the tradeoff between closeness of fit and smoothness of fit.
2
Specification
Fortran Interface
Subroutine e02ddf ( 
start, m, x, y, f, w, s, nxest, nyest, nx, lamda, ny, mu, c, fp, rank, wrk, lwrk, iwrk, liwrk, ifail) 
Integer, Intent (In) 
:: 
m, nxest, nyest, lwrk, liwrk 
Integer, Intent (Inout) 
:: 
nx, ny, ifail 
Integer, Intent (Out) 
:: 
rank, iwrk(liwrk) 
Real (Kind=nag_wp), Intent (In) 
:: 
f(m), w(m), s 
Real (Kind=nag_wp), Intent (Inout) 
:: 
x(m), y(m), lamda(nxest), mu(nyest), wrk(lwrk) 
Real (Kind=nag_wp), Intent (Out) 
:: 
c((nxest4)*(nyest4)), fp 
Character (1), Intent (In) 
:: 
start 

C Header Interface
#include <nag.h>
void 
e02ddf_ (const char *start, const Integer *m, double x[], double y[], const double f[], const double w[], const double *s, const Integer *nxest, const Integer *nyest, Integer *nx, double lamda[], Integer *ny, double mu[], double c[], double *fp, Integer *rank, double wrk[], const Integer *lwrk, Integer iwrk[], const Integer *liwrk, Integer *ifail, const Charlen length_start) 

C++ Header Interface
#include <nag.h> extern "C" {
void 
e02ddf_ (const char *start, const Integer &m, double x[], double y[], const double f[], const double w[], const double &s, const Integer &nxest, const Integer &nyest, Integer &nx, double lamda[], Integer &ny, double mu[], double c[], double &fp, Integer &rank, double wrk[], const Integer &lwrk, Integer iwrk[], const Integer &liwrk, Integer &ifail, const Charlen length_start) 
}

The routine may be called by the names e02ddf or nagf_fit_dim2_spline_sctr.
3
Description
e02ddf determines a smooth bicubic spline approximation $s\left(x,y\right)$ to the set of data points $\left({x}_{\mathit{r}},{y}_{\mathit{r}},{f}_{\mathit{r}}\right)$ with weights ${\mathit{w}}_{\mathit{r}}$, for $\mathit{r}=1,2,\dots ,m$.
The approximation domain is considered to be the rectangle $\left[{x}_{\mathrm{min}},{x}_{\mathrm{max}}\right]\times \left[{y}_{\mathrm{min}},{y}_{\mathrm{max}}\right]$, where ${x}_{\mathrm{min}}\left({y}_{\mathrm{min}}\right)$ and ${x}_{\mathrm{max}}\left({y}_{\mathrm{max}}\right)$ denote the lowest and highest data values of $x\left(y\right)$.
The spline is given in the Bspline representation
where
${M}_{i}\left(x\right)$ and
${N}_{j}\left(y\right)$ denote normalized cubic Bsplines, the former defined on the knots
${\lambda}_{i}$ to
${\lambda}_{i+4}$ and the latter on the knots
${\mu}_{j}$ to
${\mu}_{j+4}$. For further details, see
Hayes and Halliday (1974) for bicubic splines and
de Boor (1972) for normalized Bsplines.
The total numbers
${n}_{x}$ and
${n}_{y}$ of these knots and their values
${\lambda}_{1},\dots ,{\lambda}_{{n}_{x}}$ and
${\mu}_{1},\dots ,{\mu}_{{n}_{y}}$ are chosen automatically by the routine. The knots
${\lambda}_{5},\dots ,{\lambda}_{{n}_{x}4}$ and
${\mu}_{5},\dots ,{\mu}_{{n}_{y}4}$ are the interior knots; they divide the approximation domain
$\left[{x}_{\mathrm{min}},{x}_{\mathrm{max}}\right]\times \left[{y}_{\mathrm{min}},{y}_{\mathrm{max}}\right]$ into
$\left({n}_{x}7\right)\times \left({n}_{y}7\right)$ subpanels
$\left[{\lambda}_{\mathit{i}},{\lambda}_{\mathit{i}+1}\right]\times \left[{\mu}_{\mathit{j}},{\mu}_{\mathit{j}+1}\right]$, for
$\mathit{i}=4,5,\dots ,{n}_{x}4$ and
$\mathit{j}=4,5,\dots ,{n}_{y}4$. Then, much as in the curve case (see
e02bef), the coefficients
${c}_{ij}$ are determined as the solution of the following constrained minimization problem:
minimize
subject to the constraint
where: 
$\eta $ 
is a measure of the (lack of) smoothness of $s\left(x,y\right)$. Its value depends on the discontinuity jumps in $s\left(x,y\right)$ across the boundaries of the subpanels. It is zero only when there are no discontinuities and is positive otherwise, increasing with the size of the jumps (see Dierckx (1981b) for details). 

${\epsilon}_{r}$ 
denotes the weighted residual
${\mathit{w}}_{r}\left({f}_{r}s\left({x}_{r},{y}_{r}\right)\right)$, 
and 
${\mathbf{s}}$ 
is a nonnegative number to be specified by you. 
By means of the argument
${\mathbf{s}}$, ‘the smoothing factor’, you will then control the balance between smoothness and closeness of fit, as measured by the sum of squares of residuals in
(3). If
${\mathbf{s}}$ is too large, the spline will be too smooth and signal will be lost (underfit); if
${\mathbf{s}}$ is too small, the spline will pick up too much noise (overfit). In the extreme cases the method would return an interpolating spline
$\left(\theta =0\right)$ if
${\mathbf{s}}$ were set to zero, and returns the least squares bicubic polynomial
$\left(\eta =0\right)$ if
${\mathbf{s}}$ is set very large. Experimenting with
${\mathbf{s}}$values between these two extremes should result in a good compromise. (See
Section 9.2 for advice on choice of
${\mathbf{s}}$.) Note however, that this routine, unlike
e02bef and
e02dcf, does not allow
${\mathbf{s}}$ to be set exactly to zero: to compute an interpolant to scattered data,
e01saf or
e01sgf should be used.
The method employed is outlined in
Section 9.5 and fully described in
Dierckx (1981a) and
Dierckx (1981b). It involves an adaptive strategy for locating the knots of the bicubic spline (depending on the function underlying the data and on the value of
${\mathbf{s}}$), and an iterative method for solving the constrained minimization problem once the knots have been determined.
Values and derivatives of the computed spline can subsequently be computed by calling
e02def,
e02dff or
e02dhf as described in
Section 9.6.
4
References
de Boor C (1972) On calculating with Bsplines J. Approx. Theory 6 50–62
Dierckx P (1981a) An improved algorithm for curve fitting with spline functions Report TW54 Department of Computer Science, Katholieke Univerciteit Leuven
Dierckx P (1981b) An algorithm for surface fitting with spline functions IMA J. Numer. Anal. 1 267–283
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
Peters G and Wilkinson J H (1970) The least squares problem and pseudoinverses Comput. J. 13 309–316
Reinsch C H (1967) Smoothing by spline functions Numer. Math. 10 177–183
5
Arguments

1:
$\mathbf{start}$ – Character(1)
Input

On entry: determines whether calculations are to be performed afresh (Cold Start) or whether knots found in previous calls are to be used as an initial estimate of knot placement (Warm Start).
 ${\mathbf{start}}=\text{'C'}$
 The routine will build up the knot set starting with no interior knots. No values need be assigned to the arguments nx, ny, lamda, mu or wrk.
 ${\mathbf{start}}=\text{'W'}$
 The routine will restart the knotplacing strategy using the knots found in a previous call of the routine. In this case, the arguments nx, ny, lamda, mu and wrk must be unchanged from that previous call. This warm start can save much time in determining a satisfactory set of knots for the given value of s. This is particularly useful when different smoothing factors are used for the same dataset.
Constraint:
${\mathbf{start}}=\text{'C'}$ or $\text{'W'}$.

2:
$\mathbf{m}$ – Integer
Input

On entry:
$m$, the number of data points.
The number of data points with nonzero weight (see
w) must be at least
$16$.

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

4:
$\mathbf{y}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) array
Input

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

On entry: ${\mathbf{x}}\left(\mathit{r}\right)$, ${\mathbf{y}}\left(\mathit{r}\right)$, ${\mathbf{f}}\left(\mathit{r}\right)$ must be set to the coordinates of $\left({x}_{\mathit{r}},{y}_{\mathit{r}},{f}_{\mathit{r}}\right)$, the $\mathit{r}$th data point, for $\mathit{r}=1,2,\dots ,m$. The order of the data points is immaterial.

6:
$\mathbf{w}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) array
Input

On entry:
${\mathbf{w}}\left(\mathit{r}\right)$ must be set to
${\mathit{w}}_{\mathit{r}}$, the
$\mathit{r}$th value in the set of weights, for
$\mathit{r}=1,2,\dots ,m$. Zero weights are permitted and the corresponding points are ignored, except when determining
${x}_{\mathrm{min}}$,
${x}_{\mathrm{max}}$,
${y}_{\mathrm{min}}$ and
${y}_{\mathrm{max}}$ (see
Section 9.4). For advice on the choice of weights, see
Section 2.1.2 in the
E02 Chapter Introduction.
Constraint:
the number of data points with nonzero weight must be at least $16$.

7:
$\mathbf{s}$ – Real (Kind=nag_wp)
Input

On entry: the smoothing factor,
s.
For advice on the choice of
s, see
Sections 3 and
9.2.
Constraint:
${\mathbf{s}}>0.0$.

8:
$\mathbf{nxest}$ – Integer
Input

9:
$\mathbf{nyest}$ – Integer
Input

On entry: an upper bound for the number of knots
${n}_{x}$ and
${n}_{y}$ required in the
$x$ and
$y$directions respectively.
In most practical situations,
${\mathbf{nxest}}={\mathbf{nyest}}=4+\sqrt{m/2}$ is sufficient. See also
Section 9.3.
Constraint:
${\mathbf{nxest}}\ge 8$ and ${\mathbf{nyest}}\ge 8$.

10:
$\mathbf{nx}$ – Integer
Input/Output

On entry: if the warm start option is used, the value of
nx must be left unchanged from the previous call.
On exit: the total number of knots, ${n}_{x}$, of the computed spline with respect to the $x$ variable.

11:
$\mathbf{lamda}\left({\mathbf{nxest}}\right)$ – Real (Kind=nag_wp) array
Input/Output

On entry: if the warm start option is used, the values ${\mathbf{lamda}}\left(1\right),{\mathbf{lamda}}\left(2\right),\dots ,{\mathbf{lamda}}\left({\mathbf{nx}}\right)$ must be left unchanged from the previous call.
On exit: contains the complete set of knots
${\lambda}_{i}$ associated with the
$x$ variable, i.e., the interior knots
${\mathbf{lamda}}\left(5\right),{\mathbf{lamda}}\left(6\right),\dots ,{\mathbf{lamda}}\left({\mathbf{nx}}4\right)$ as well as the additional knots
and
needed for the Bspline representation (where
${x}_{\mathrm{min}}$ and
${x}_{\mathrm{max}}$ are as described in
Section 3).

12:
$\mathbf{ny}$ – Integer
Input/Output

On entry: if the warm start option is used, the value of
ny must be left unchanged from the previous call.
On exit: the total number of knots, ${n}_{y}$, of the computed spline with respect to the $y$ variable.

13:
$\mathbf{mu}\left({\mathbf{nyest}}\right)$ – Real (Kind=nag_wp) array
Input/Output

On entry: if the warm start option is used, the values ${\mathbf{mu}}\left(1\right),{\mathbf{mu}}\left(2\right),\dots ,{\mathbf{mu}}\left({\mathbf{ny}}\right)$ must be left unchanged from the previous call.
On exit: contains the complete set of knots
${\mu}_{i}$ associated with the
$y$ variable, i.e., the interior knots
${\mathbf{mu}}\left(5\right),{\mathbf{mu}}\left(6\right),\dots ,{\mathbf{mu}}\left({\mathbf{ny}}4\right)$ as well as the additional knots
and
needed for the Bspline representation (where
${y}_{\mathrm{min}}$ and
${y}_{\mathrm{max}}$ are as described in
Section 3).

14:
$\mathbf{c}\left(\left({\mathbf{nxest}}4\right)\times \left({\mathbf{nyest}}4\right)\right)$ – Real (Kind=nag_wp) array
Output

On exit: the coefficients of the spline approximation.
${\mathbf{c}}\left(\left({n}_{y}4\right)\times \left(i1\right)+j\right)$ is the coefficient
${c}_{ij}$ defined in
Section 3.

15:
$\mathbf{fp}$ – Real (Kind=nag_wp)
Output

On exit: the weighted sum of squared residuals,
$\theta $, of the computed spline approximation.
fp should equal
s within a relative tolerance of
$0.001$ unless
${\mathbf{nx}}={\mathbf{ny}}=8$, when the spline has no interior knots and so is simply a bicubic polynomial. For knots to be inserted,
s must be set to a value below the value of
fp produced in this case.

16:
$\mathbf{rank}$ – Integer
Output

On exit: gives the rank of the system of equations used to compute the final spline (as determined by a suitable machinedependent threshold). When
${\mathbf{rank}}=\left({\mathbf{nx}}4\right)\times \left({\mathbf{ny}}4\right)$, the solution is unique; otherwise the system is rankdeficient and the minimumnorm solution is computed. The latter case may be caused by too small a value of
s.

17:
$\mathbf{wrk}\left({\mathbf{lwrk}}\right)$ – Real (Kind=nag_wp) array
Communication Array

If the warm start option is used, on entry, the value of ${\mathbf{wrk}}\left(1\right)$ must be left unchanged from the previous call.
This array is used as workspace.

18:
$\mathbf{lwrk}$ – Integer
Input

On entry: the dimension of the array
wrk as declared in the (sub)program from which
e02ddf is called.
Constraint:
${\mathbf{lwrk}}\ge \left(7\times \mathit{u}\times \mathit{v}+25\times \mathit{w}\right)\times \left(\mathit{w}+1\right)+2\times \left(\mathit{u}+\mathit{v}+4\times {\mathbf{m}}\right)+23\times \mathit{w}+56$,
where
$\mathit{u}={\mathbf{nxest}}4$,
$\mathit{v}={\mathbf{nyest}}4$ and
$\mathit{w}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(\mathit{u},\mathit{v}\right)$.
For some problems, the routine may need to compute the minimal least squares solution of a rankdeficient system of linear equations (see
Section 3). The amount of workspace required to solve such problems will be larger than specified by the value given above, which must be increased by an amount,
lwrk2 say. An upper bound for
lwrk2 is given by
$4\times \mathit{u}\times \mathit{v}\times \mathit{w}+2\times \mathit{u}\times \mathit{v}+4\times \mathit{w}$, where
$\mathit{u}$,
$\mathit{v}$ and
$\mathit{w}$ are as above. However, if there are enough data points, scattered uniformly over the approximation domain, and if the smoothing factor
s is not too small, there is a good chance that this extra workspace is not needed. A lot of memory might therefore be saved by assuming that no additional workspace is required (
$\mathit{lwrk2}=0$).

19:
$\mathbf{iwrk}\left({\mathbf{liwrk}}\right)$ – Integer array
Workspace

20:
$\mathbf{liwrk}$ – Integer
Input

On entry: the dimension of the array
iwrk as declared in the (sub)program from which
e02ddf is called.
Constraint:
${\mathbf{liwrk}}\ge {\mathbf{m}}+2\times \left({\mathbf{nxest}}7\right)\times \left({\mathbf{nyest}}7\right)$.

21:
$\mathbf{ifail}$ – Integer
Input/Output

On entry:
ifail must be set to
$0$,
$1\text{or}1$. 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
$1\text{or}1$ 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 $\mathbf{1}\text{or}\mathbf{1}$ 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, ${\mathbf{liwrk}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{liwrk}}\ge \u2329\mathit{\text{value}}\u232a$.
On entry, ${\mathbf{lwrk}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{lwrk}}\ge \u2329\mathit{\text{value}}\u232a$.
On entry, $\mathrm{MM}<16$, where $\mathrm{MM}$ is the number of points with nonzero weight: $\mathrm{MM}=\u2329\mathit{\text{value}}\u232a$.
On entry, ${\mathbf{nxest}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{nxest}}\ge \u2329\mathit{\text{value}}\u232a$.
On entry, ${\mathbf{nyest}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{nyest}}\ge \u2329\mathit{\text{value}}\u232a$.
On entry, ${\mathbf{s}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{s}}>0.0$.
On entry, ${\mathbf{start}}\ne \text{'W'}$ or $\text{'C'}$: ${\mathbf{start}}=\u2329\mathit{\text{value}}\u232a$.
 ${\mathbf{ifail}}=2$

On entry, all the values in array
x are equal.
On entry, all the values in array
y are equal.
 ${\mathbf{ifail}}=3$

The number of knots needed in one direction is greater than
nxest or
nyest:
nxest,
${\mathbf{nyest}}=\u2329\mathit{\text{value}}\u232a,\u2329\mathit{\text{value}}\u232a$. Possibly
s is too small:
${\mathbf{s}}=\u2329\mathit{\text{value}}\u232a$.
 ${\mathbf{ifail}}=4$

No more knots added; the number of
$B$spline coefficients already exceeds
m. Either
m or
s is probably too small:
${\mathbf{m}}=\u2329\mathit{\text{value}}\u232a$ and
${\mathbf{s}}=\u2329\mathit{\text{value}}\u232a$.
 ${\mathbf{ifail}}=5$

No more knots added; the additional knot would coincide with an old one. Possibly an inaccurate data point has too large a weight, or
s is too small:
${\mathbf{s}}=\u2329\mathit{\text{value}}\u232a$.
 ${\mathbf{ifail}}=6$

The iterative process has failed to converge. Possibly
s is too small:
${\mathbf{s}}=\u2329\mathit{\text{value}}\u232a$.
 ${\mathbf{ifail}}=7$

lwrk is too small to compute the minimal least squares solution:
${\mathbf{lwrk}}=\u2329\mathit{\text{value}}\u232a$. Minimum requested value for
lwrk is
$\u2329\mathit{\text{value}}\u232a$; Safe requested value for
lwrk is
$\u2329\mathit{\text{value}}\u232a$.
 ${\mathbf{ifail}}=99$
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.
If
${\mathbf{ifail}}={\mathbf{3}}$,
${\mathbf{4}}$,
${\mathbf{5}}$ or
${\mathbf{6}}$, a spline approximation is returned, but it fails to satisfy the fitting criterion (see
(2) and
(3) in
Section 3 – perhaps only by a small amount, however.
7
Accuracy
On successful exit, the approximation returned is such that its weighted sum of squared residuals
fp is equal to the smoothing factor
${\mathbf{s}}$, up to a specified relative tolerance of
$0.001$ – except that if
${n}_{x}=8$ and
${n}_{y}=8$,
fp may be significantly less than
${\mathbf{s}}$: in this case the computed spline is simply the least squares bicubic polynomial approximation of degree
$3$, i.e., a spline with no interior knots.
8
Parallelism and Performance
e02ddf 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 of e02ddf depends on the complexity of the shape of the data, the value of the smoothing factor ${\mathbf{s}}$, and the number of data points. If e02ddf is to be called for different values of ${\mathbf{s}}$, much time can be saved by setting ${\mathbf{start}}=\text{'W'}$ after the first call.
It should be noted that choosing ${\mathbf{s}}$ very small considerably increases computation time.
If the weights have been correctly chosen (see
Section 2.1.2 in the
E02 Chapter Introduction), the standard deviation of
${\mathit{w}}_{r}{f}_{r}$ would be the same for all
$r$, equal to
$\sigma $, say. In this case, choosing the smoothing factor
${\mathbf{s}}$ in the range
${\sigma}^{2}\left(m\pm \sqrt{2m}\right)$, as suggested by
Reinsch (1967), is likely to give a good start in the search for a satisfactory value. Otherwise, experimenting with different values of
${\mathbf{s}}$ will be required from the start.
In that case, in view of computation time and memory requirements, it is recommended to start with a very large value for
${\mathbf{s}}$ and so determine the least squares bicubic polynomial; the value returned for
fp, call it
${{\mathbf{fp}}}_{0}$, gives an upper bound for
${\mathbf{s}}$. Then progressively decrease the value of
${\mathbf{s}}$ to obtain closer fits – say by a factor of
$10$ in the beginning, i.e.,
${\mathbf{s}}={{\mathbf{fp}}}_{0}/10$,
${\mathbf{s}}={{\mathbf{fp}}}_{0}/100$, and so on, and more carefully as the approximation shows more details.
To choose
${\mathbf{s}}$ very small is strongly discouraged. This considerably increases computation time and memory requirements. It may also cause rankdeficiency (as indicated by the argument
rank) and endanger numerical stability.
The number of knots of the spline returned, and their location, generally depend on the value of ${\mathbf{s}}$ and on the behaviour of the function underlying the data. However, if e02ddf is called with ${\mathbf{start}}=\text{'W'}$, the knots returned may also depend on the smoothing factors of the previous calls. Therefore if, after a number of trials with different values of ${\mathbf{s}}$ and ${\mathbf{start}}=\text{'W'}$, a fit can finally be accepted as satisfactory, it may be worthwhile to call e02ddf once more with the selected value for ${\mathbf{s}}$ but now using ${\mathbf{start}}=\text{'C'}$. Often, e02ddf then returns an approximation with the same quality of fit but with fewer knots, which is therefore better if data reduction is also important.
The number of knots may also depend on the upper bounds
nxest and
nyest. Indeed, if at a certain stage in
e02ddf the number of knots in one direction (say
${n}_{x}$) has reached the value of its upper bound (
nxest), then from that moment on all subsequent knots are added in the other
$\left(y\right)$ direction. This may indicate that the value of
nxest is too small. On the other hand, it gives you the option of limiting the number of knots the routine locates in any direction. For example, by setting
${\mathbf{nxest}}=8$ (the lowest allowable value for
nxest), you can indicate that you want an approximation which is a simple cubic polynomial in the variable
$x$.
The fit obtained is not defined outside the rectangle $\left[{\lambda}_{4},{\lambda}_{{n}_{x}3}\right]\times \left[{\mu}_{4},{\mu}_{{n}_{y}3}\right]$. The reason for taking the extreme data values of $x$ and $y$ for these four knots is that, as is usual in data fitting, the fit cannot be expected to give satisfactory values outside the data region. If, nevertheless, you require values over a larger rectangle, this can be achieved by augmenting the data with two artificial data points $\left(a,c,0\right)$ and $\left(b,d,0\right)$ with zero weight, where $\left[a,b\right]\times \left[c,d\right]$ denotes the enlarged rectangle.
First suitable knot sets are built up in stages (starting with no interior knots in the case of a cold start but with the knot set found in a previous call if a warm start is chosen). At each stage, a bicubic spline is fitted to the data by least squares and
$\theta $, the sum of squares of residuals, is computed. If
$\theta >{\mathbf{s}}$, a new knot is added to one knot set or the other so as to reduce
$\theta $ at the next stage. The new knot is located in an interval where the fit is particularly poor. Sooner or later, we find that
$\theta \le {\mathbf{s}}$ and at that point the knot sets are accepted. The routine then goes on to compute a spline which has these knot sets and which satisfies the full fitting criterion specified by
(2) and
(3). The theoretical solution has
$\theta ={\mathbf{s}}$. The routine computes the spline by an iterative scheme which is ended when
$\theta ={\mathbf{s}}$ within a relative tolerance of
$0.001$. The main part of each iteration consists of a linear least squares computation of special form, done in a similarly stable and efficient manner as in
e02daf. As there also, the minimal least squares solution is computed wherever the linear system is found to be rankdeficient.
An exception occurs when the routine finds at the start that, even with no interior knots ($\mathrm{N}=8$), the least squares spline already has its sum of squares of residuals $\text{}\le {\mathbf{s}}$. In this case, since this spline (which is simply a bicubic polynomial) also has an optimal value for the smoothness measure $\eta $, namely zero, it is returned at once as the (trivial) solution. It will usually mean that ${\mathbf{s}}$ has been chosen too large.
For further details of the algorithm and its use see
Dierckx (1981b).
The values of the computed spline at the points
$\left({x}_{\mathit{r}},{y}_{\mathit{r}}\right)$, for
$\mathit{r}=1,2,\dots ,n$, may be obtained in the real array
ff (see
e02def), of length at least
$n$, by the following call:
ifail = 0
Call e02def(n,nx,ny,x,y,lamda,mu,c,ff,wrk,iwrk,ifail)
where
$\mathtt{N}=n$ and the coordinates
${x}_{r}$,
${y}_{r}$ are stored in
$\mathtt{X}\left(k\right)$,
$\mathtt{Y}\left(k\right)$.
PX and
PY have the same values as
nx and
ny as output from
e02ddf, and
LAMDA,
MU and
C have the same values as
lamda,
mu and
c output from
e02ddf.
WRK is a real workspace array of length at least
$\mathtt{PY}4$, and
IWRK is an integer workspace array of length at least
$\mathtt{PY}4$.
To evaluate the computed spline on a
${k}_{x}$ by
${k}_{y}$ rectangular grid of points in the
$x$
$y$ plane, which is defined by the
$x$ coordinates stored in
$\mathtt{X}\left(\mathit{q}\right)$, for
$\mathit{q}=1,2,\dots ,{k}_{x}$, and the
$y$ coordinates stored in
$\mathtt{Y}\left(\mathit{r}\right)$, for
$\mathit{r}=1,2,\dots ,{k}_{y}$, returning the results in the real array
ff (see
e02dff) which is of length at least
${\mathbf{mx}}\times {\mathbf{my}}$, the following call may be used:
ifail = 0
Call e02dff(kx,ky,nx,ny,tx,ty,lamda,mu,c,fg,wrk,lwrk,
* iwrk,liwrk,ifail)
where
$\mathtt{KX}={k}_{x}$,
$\mathtt{KY}={k}_{y}$.
NX,
NY,
LAMDA,
MU and
C have the same values as
nx,
ny,
lamda,
mu and
c output from
e02ddf.
WRK is a real workspace array of length at least
$\mathtt{LWRK}=\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(\mathit{nwrk1},\mathit{nwrk2}\right)$, where
$\mathit{nwrk1}=\mathtt{KX}\times 4+\mathtt{PX}$ and
$\mathit{nwrk2}=\mathtt{KY}\times 4+\mathtt{PY}$.
IWRK is an integer workspace array of length at least
$\mathtt{LIWRK}=\mathtt{KY}+\mathtt{PY}4$ if
$\mathit{nwrk1}\ge \mathit{nwrk2}$, or
$\mathtt{KX}+\mathtt{PX}4$ otherwise.
The result of the spline evaluated at grid point $\left(q,r\right)$ is returned in element $\left(\mathtt{KY}\times \left(q1\right)+r\right)$ of the array FG.
10
Example
This example reads in a value of
m, followed by a set of
m data points
$\left({x}_{r},{y}_{r},{f}_{r}\right)$ and their weights
${\mathit{w}}_{r}$. It then calls
e02ddf to compute a bicubic spline approximation for one specified value of
s, and prints the values of the computed knots and Bspline coefficients. Finally it evaluates the spline at a small sample of points on a rectangular grid.
10.1
Program Text
10.2
Program Data
10.3
Program Results