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Because the toolbox can handle splines with *vector* coefficients,
it is easy to implement interpolation or approximation to gridded data by tensor product splines, as the following illustration
is meant to show. You can also run the example “Bivariate Tensor
Product Splines”.

To be sure, most tensor product spline approximation to gridded
data can be obtained directly with one of the spline construction
commands, like `spapi`

or `csape`

,
in this toolbox, without concern for the details discussed in this
example. Rather, this example is meant to illustrate the theory behind
the tensor product construction, and this will be of help in situations
not covered by the construction commands in this toolbox.

This section discusses these aspects of the tensor product spline problem:

Consider, for example, least squares approximation to given
data *z*(*i*,*j*)=*f*(*x*(*i*),*y*(*j*)),*i*=1:*Nx*,*j*=1:*Ny*.
You take the data from a function used extensively by Franke for the testing
of schemes for surface fitting (see R. Franke, “A critical
comparison of some methods for interpolation of scattered data,” *Naval
Postgraduate School Techn. Rep. NPS-53-79-003*, March 1979).
Its domain is the unit square. You choose a few more data sites in
the *x*-direction than the *y*-direction;
also, for a better definition, you use higher data density near the
boundary.

x = sort([(0:10)/10,.03 .07, .93 .97]); y = sort([(0:6)/6,.03 .07, .93 .97]); [xx,yy] = ndgrid(x,y); z = franke(xx,yy);

Treat these data as coming from a vector-valued
function, namely, the function of *y* whose value
at *y*(*j*) is the vector *z*(:,*j*),
all *j*. For no particular reason, choose to approximate
this function by a vector-valued parabolic spline, with three uniformly spaced interior knots. This means that you choose
the spline order and the knot sequence for this vector-valued spline
as

ky = 3; knotsy = augknt([0,.25,.5,.75,1],ky);

and then use `spap2`

to provide the
least squares approximant to the data:

sp = spap2(knotsy,ky,y,z);

In effect, you are finding simultaneously the discrete least squares
approximation from *S*

to
each of the _{ky,knotsy}`Nx`

data sets

$$\begin{array}{cc}{\left(y\left(j\right),z\left(i,j\right)\right)}_{j=1}^{Ny},& i=1:Nx\end{array}$$

In particular, the statements

yy = -.1:.05:1.1; vals = fnval(sp,yy);

provide the array `vals`

, whose entry `vals(i,j)`

can
be taken as an approximation to the value *f*(*x*(*i*),*yy*(*j*))of
the underlying function *f* at the mesh-point *x*(*i*),*yy*(*j*)
because `vals(:,j)`

is the value at *yy*(*j*)
of the approximating spline curve in `sp`

.

This is evident in the following figure, obtained by the command:

mesh(x,yy,vals.'), view(150,50)

Note the use of `vals.'`

, in the `mesh`

command,
needed because of the MATLAB^{®} matrix-oriented view when plotting
an array. This can be a serious problem in bivariate approximation
because there it is customary to think of *z*(*i*, *j*)
as the function value at the point (*x*(*i*), *y*(*j*)),
while MATLAB thinks of *z*(*i*,* j*)
as the function value at the point (*x*(*j*), *y*(*i*)).

**A Family of Smooth Curves Pretending to Be
a Surface**

Note that both the first two and the last two values on each
smooth curve are actually zero because both the first two and the
last two sites in `yy`

are outside the basic interval for the spline in `sp`

.

Note also the ridges. They confirm that you are plotting smooth curves in one direction only.

To get an actual surface, you now have to go a step further.
Look at the coefficients `coefsy`

of the spline in `sp`

:

coefsy = fnbrk(sp,'coefs');

Abstractly, you can think of the spline in `sp`

as
the function

$$y|\to {\displaystyle \sum _{r}coefsy\left(:,r\right){B}_{r,ky}\left(y\right)}$$

with the *i*th entry `coefsy(i,r)`

of
the vector coefficient `coefsy(:,r)`

corresponding
to *x*(*i*), for all *i*.
This suggests approximating each coefficient vector `coefsy(q,:)`

by
a spline of the same order `kx`

and with the same
appropriate knot sequence `knotsx`

. For no particular
reason, this time use *cubic* splines with *four* uniformly
spaced interior knots:

kx = 4; knotsx = augknt([0:.2:1],kx); sp2 = spap2(knotsx,kx,x,coefsy.');

Note that `spap2`

(`knots,k,x,fx`

)
expects `fx`

(`:,j`

) to be the datum
at `x(j)`

, i.e., expects each *column* of `fx`

to
be a function value. To fit the datum `coefsy(q, :)`

at *x*(*q*),
for all *q*, present `spap2`

with
the *transpose* of `coefsy`

.

Now consider the transpose of the coefficients `cxy`

of
the resulting spline *curve*:

coefs = fnbrk(sp2,'coefs').';

It provides the *bivariate* spline approximation

$$\left(x,y\right)|\to {\displaystyle \sum _{q}{\displaystyle \sum _{r}coefs\left(q,r\right){B}_{q,kx}\left(x\right){B}_{r,ky}\left(y\right)}}$$

to the original data

$$\left(x\left(i\right),y\left(j\right)\right)|\to z\left(x\left(i\right),y\left(j\right)\right),i=1:Nx,j=1:Ny$$

To plot this spline surface over a grid, e.g., the grid

xv = 0:.025:1; yv = 0:.025:1;

you can do the following:

This results in the following figure.

**Spline Approximation to Franke's Function**

This makes good sense because `spcol(knotsx,kx,xv)`

is
the matrix whose (*i*,*q*)th entry
equals the value B_{q,kx}(*xv*(*i*))
at *xv*(*i*) of the *q*th
B-spline of order `kx`

for the knot sequence `knotsx`

.

Because the matrices `spcol(knotsx,kx,xv)`

and `spcol(knotsy,ky,yv)`

are banded, it may be more efficient,
though perhaps more memory-consuming, for *large* `xv`

and `yv`

to
make use of `fnval`

, as follows:

value2 = ... fnval(spmak(knotsx,fnval(spmak(knotsy,coefs),yv).'),xv).';

This is, in fact, what happens internally when `fnval`

is
called directly with a tensor product spline, as in

value2 = fnval(spmak({knotsx,knotsy},coefs),{xv,yv});

Here is the calculation of the relative error, i.e., the difference between the given data and the value of the approximation at those data sites as compared with the magnitude of the given data:

errors = z - spcol(knotsx,kx,x)*coefs*spcol(knotsy,ky,y).'; disp( max(max(abs(errors)))/max(max(abs(z))) )

The output is `0.0539`

, perhaps not too impressive.
However, the coefficient array was only of size `8 6`

disp(size(coefs))

to fit a data array of size `15 11`

.

disp(size(z))

The approach followed here seems *biased*,
in the following way. First think of the given data `z`

as
describing a vector-valued function of *y*, and then
treat the matrix formed by the vector coefficients of the approximating
curve as describing a vector-valued function of *x*.

What happens when you take things in the opposite order, i.e.,
think of `z`

as describing a vector-valued function
of *x*, and then treat the matrix made up from the
vector coefficients of the approximating curve as describing a vector-valued
function of *y*?

Perhaps surprisingly, the final approximation is the same, up to roundoff. Here is the numerical experiment.

First, fit a spline curve to the data, but this time with *x* as
the independent variable, hence it is the *rows* of `z`

that
now become the data values. Correspondingly, you must supply `z.'`

,
rather than `z`

, to `spap2`

,

spb = spap2(knotsx,kx,x,z.');

thus obtaining a spline approximation to all the curves (*x* ; *z *(:, *j*)).
In particular, the statement

valsb = fnval(spb,xv).';

provides the matrix `valsb`

, whose entry ```
valsb(i,
j)
```

can be taken as an approximation to the value *f*(*xv*(*i*),*y*(*j*))
of the underlying function *f* at the mesh-point
(*xv*(*i*),*y*(*j*)).
This is evident when you plot `valsb`

using `mesh`

:

mesh(xv,y,valsb.'), view(150,50)

**Another Family of Smooth Curves Pretending
to Be a Surface**

Note the ridges. They confirm that you are, once again, plotting smooth curves in one direction only. But this time the curves run in the other direction.

Now comes the second step, to get the actual surface. First, extract the coefficients:

coefsx = fnbrk(spb,'coefs');

Then fit each coefficient vector `coefsx(r,:)`

by
a spline of the same order `ky`

and with the same
appropriate knot sequence `knotsy`

:

Note that, once again, you need to transpose the coefficient
array from `spb`

, because `spap2`

takes
the columns of its last input argument as the data values.

Correspondingly, there is now no need to transpose the coefficient
array `coefsb`

of the resulting *curve*:

coefsb = fnbrk(spb2,'coefs');

The claim is that `coefsb`

equals the earlier
coefficient array `coefs`

, up to round-off, and here
is the test:

disp( max(max(abs(coefs - coefsb))) )

The output is `1.4433e-15`

.

The explanation is simple enough: The coefficients `c`

of
the spline *s* contained in `sp = spap2(knots,k,x,y)`

depend *linearly* on
the input values *y*. This implies, given that both `c`

and `y`

are
1-row matrices, that there is some matrix *A*=*A*_{knots,k,x} so
that

$$c=y{A}_{\text{knots},k,x}$$

for any data `y`

. This statement even holds
when `y`

is a *matrix*, of size *d*-by-*N*,
say, in which case each datum *y*(:,*j*)
is taken to be a point in *R*^{d},
and the resulting spline is correspondingly *d*-vector-valued,
hence its coefficient array `c`

is of size *d*-by-`n`

,
with n = `length(knots)-k`

.

In particular, the statements

sp = spap2(knotsy,ky,y,z); coefsy =fnbrk(sp,'coefs');

provide us with the matrix `coefsy`

that satisfies

$$\text{coefs}y=z.{A}_{\text{knotsy,ky,y}}$$

The subsequent computations

sp2 = spap2(knotsx,kx,x,coefsy.'); coefs = fnbrk(sp2,'coefs').';

generate the coefficient array `coefs`

, which,
taking into account the two transpositions, satisfies

$$\begin{array}{c}\text{coefs}=\left(\left(z{A}_{\text{knotsy,ky,y}}\right)\text{'}.{A}_{\text{knotsx,kx,x}}\right)\text{'}\\ =\left({A}_{\text{knotsx,kx,x}}\right)\text{'}.z.{A}_{\text{knotsy,ky,y}}\end{array}$$

In the second, alternative, calculation, you first computed

spb = spap2(knotsx,kx,x,z.'); coefsx = fnbrk(spb,'coefs');

hence `coefsx`

=z'.A_{knotsx,kx,x}.
The subsequent calculation

spb2 = spap2(knotsy,ky,y,coefsx.'); coefsb = fnbrk(spb,'coefs');

then provided` `

$$\text{coefsb}=\text{coefsx}.\text{'}.{A}_{\text{knotsy,ky,y}}=\left({A}_{\text{knotsx,kx,x}}\right).\text{'}.z.{A}_{\text{knotsy,ky,}y}$$

Consequently, `coefsb`

= `coefs`

.

The second approach is more symmetric than the first in that
transposition takes place in each call to `spap2`

and
nowhere else. This approach can be used for approximation to gridded
data in any number of variables.

If, for example, the given data over a *three*-dimensional
grid are contained in some three-dimensional array `v`

of
size `[Nx,Ny,Nz]`

, with `v(i,j,k)`

containing
the value *f*(*x*(*i*),*y*(*j*),*z*(*k*)),
then you would start off with

coefs = reshape(v,Nx,Ny*Nz);

Assuming that `n`

j = `knots`

j
- `kj`

, for j =` x,y,z`

, you would
then proceed as follows:

sp = spap2(knotsx,kx,x,coefs.'); coefs = reshape(fnbrk(sp,'coefs'),Ny,Nz*nx); sp = spap2(knotsy,ky,y,coefs.'); coefs = reshape(fnbrk(sp,'coefs'),Nz,nx*ny); sp = spap2(knotsz,kz,z,coefs.'); coefs = reshape(fnbrk(sp,'coefs'),nx,ny*nz);

See Chapter 17 of *PGS* or [C. de Boor, “Efficient
computer manipulation of tensor products,” *ACM Trans.
Math. Software 5* (1979), 173–182; Corrigenda, 525]
for more details. The same references also make clear that there is
nothing special here about using least squares approximation. Any
approximation process, including spline interpolation, whose resulting
approximation has coefficients that depend linearly on the given data,
can be extended in the same way to a multivariate approximation process
to gridded data.

This is exactly what is used in the spline construction commands `csapi`

, `csape`

, `spapi`

, `spaps`

,
and `spap2`

, when gridded data are to be fitted.
It is also used in `fnval`

, when a tensor product
spline is to be evaluated on a grid.

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