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Curve Fitting Toolbox™ spline functions contain versions of
the essential MATLAB^{®} programs of the B-spline package (extended
to handle also *vector*-valued splines) as described
in *A Practical Guide to Splines,* (Applied Math.
Sciences Vol. 27, Springer Verlag, New York (1978), xxiv + 392p; revised
edition (2001), xviii+346p), hereafter referred to as *PGS*. The toolbox
makes it easy to create and work with piecewise-polynomial functions.

The typical use envisioned for this toolbox involves the construction
and subsequent use of a piecewise-polynomial approximation. This construction
would involve data fitting, but there is a wide range of possible
data that could be fit. In the simplest situation, one is given points
(*t*i*,y*i) and is looking for a
piecewise-polynomial function *f* that satisfies *f*(*t*i)
= *y*i, all *i*, more or less. An
exact fit would involve* interpolation*, an approximate
fit might involve *least-squares approximation* or
the *smoothing spline*. But the function to be
approximated may also be described in more implicit ways, for example as the solution of a differential
or integral equation.
In such a case, the data would be of the form (*Af*)(*t*i),
with *A* some differential or integral operator.
On the other hand, one might want to construct a spline *curve* whose
exact location is less important than is its overall shape. Finally,
in all of this, one might be looking for functions of more than one
variable, such as *tensor product splines*.

Care has been taken to make this work as painless and intuitive
as possible. In particular, the user need not worry about just how
splines are constructed or stored for later use, nor need the casual
user worry about such items as “breaks” or “knots”
or “coefficients”. It is enough to know that each function
constructed is just another variable that is freely usable as input
(where appropriate) to many of the commands, including all commands
beginning with `fn`

, which stands for `function`

.
At times, it may be also useful to know that, internal to the toolbox,
splines are stored in different forms, with the command `fn2fm`

available
to convert between forms.

At present, the toolbox supports two major forms for the representation
of piecewise-polynomial functions, because each has been found to
be superior to the other in certain common situations. The B-form
is particularly useful during the construction of a spline, while
the ppform is more efficient when the piecewise-polynomial function
is to be evaluated extensively. These two forms are almost exactly
the B-representation
and the pp
representation used in *A Practical Guide to Splines*.

But, over the years, the Curve Fitting Toolbox spline functions
have gone beyond the programs in *A Practical Guide to Splines*.
The toolbox now supports the `scattered translates' form, or stform,
in order to handle the construction and use of bivariate thin-plate
splines, and also two ways to represent rational splines, the rBform
and the rpform, in order to handle NURBS.

Splines can be very effective for data fitting because the linear
systems to be solved for this are banded, hence the work needed for
their solution, done properly, grows only linearly with the number
of data points. In particular, the MATLAB sparse matrix facilities
are used in the Curve Fitting Toolbox spline functions when that
is more efficient than the toolbox's own equation solver, `slvblk`

,
which relies on the fact that some of the linear systems here are
even almost block diagonal.

All polynomial
spline construction commands are equipped to produce bivariate (or
even multivariate) piecewise-polynomial functions as tensor products
of the univariate functions used here, and the various `fn...`

commands
also work for these multivariate functions.

There are various examples, all accessible through the Help browser. You are strongly urged to
have a look at some of them, or at the `splinetool`

, to help you
work with splines.

Consider the set

$$S:={\Pi}_{\xi ,k}^{\mu}$$

of all (scalar-valued) piecewise-polynomials of order *k* with
breaks *ξ*_{1} < ... < *ξ*_{l + 1} that, for
*i* = 2...*l*, may have a jump
across *ξ _{i}* in its

Accordingly, *S* contains a basis (in fact, infinitely many bases), that is, a sequence
*f*_{1},...,*f _{n}*
so that every

$$f(x)={\displaystyle \sum _{j=1}^{n}{f}_{j}(x){a}_{j}},$$

for suitable coefficients *a _{j}*. The number

In particular, according to the Curry-Schoenberg Theorem, our space *S* has a basis
consisting of B-splines, namely the sequence of all B-splines of the form $$B\left(\xb7|{t}_{j},\mathrm{...},{t}_{j+k}\right)$$, *j* = 1...*n*, with
the knot sequence *t* obtained from the break sequence
*ξ* and the sequence *µ* by the following
conditions:

Have both

*ξ*_{1}and*ξ*_{l + 1}occur in*t*exactly*k*timesFor each

*i*= 2:*l*, have*ξ*occur in_{i}*t*exactly*k*–*µ*times_{i}Make sure the sequence is nondecreasing and only contains elements from

*ξ*

Note the correspondence between the multiplicity of a knot and the smoothness of
the spline across that knot. In particular, at a simple knot, that is a knot that appears exactly once in the knot
sequence, only the (*k* – 1)st derivative may be
discontinuous.

The MATLAB technical computing environment provides spline approximation via the
command `spline`

. If called in the form ```
cs =
```

`spline`

`(x,y)`

, it
returns the ppform of the cubic spline with break sequence `x`

that
takes the value `y(i)`

at `x(i)`

, all
`i`

, and satisfies the not-a-knot end condition. In other
words, the command `cs = spline(x,y)`

gives the same
result as the command `cs = `

`csapi`

`(x,y)`

available in the Curve Fitting Toolbox spline functions. But only the latter also works when
`x`

,`y`

describe multivariate gridded data. In
MATLAB, cubic spline interpolation to multivariate gridded data is provided
by the command `interpn`

`(x1,...,xd,v,y1,...,yd,'spline') `

which
returns values of the interpolating tensor product cubic spline at the grid
specified by `y1`

,...,`yd`

.

Further, any of the Curve Fitting Toolbox spline `fn...`

commands can be applied to the output
of the MATLAB
`spline(x,y)`

command, with simple versions of the Curve Fitting Toolbox spline commands `fnval`

, `ppmak`

,
`fnbrk `

available directly in MATLAB, as the commands `ppval`

, `mkpp`

,
`unmkpp`

, respectively.

The Curve Fitting Toolbox spline functions started out as an extension of the MATLAB environment of interest to experts in spline approximation, to aid
them in the construction and testing of new methods of spline approximation. Such
people will have mastered the material in *A Practical Guide to
Splines*.

However, the basic commands for constructing and using spline approximations are set up to be usable with no more knowledge than it takes to understand what it means to, say, construct an interpolant or a least squares approximant to some data, or what it means to differentiate or integrate a function.

With that in mind, there are sections, like Cubic Spline Interpolation, that are meant even for the novice, while sections devoted to a detailed example, like the one on constructing a Chebyshev spline or on constructing and using tensor products, are meant for users interested in developing their own spline commands.

The Curve Fitting Toolbox spline functions can handle *vector*-valued
splines, i.e., splines whose values lie in R^{d}.
Since MATLAB started out with just one variable type, that of
a matrix, there is even now some uncertainty about how to deal with
vectors, i.e., lists of numbers. MATLAB sometimes stores such
a list in a matrix with just one row, and other times in a matrix
with just one column. In the first instance, such a *1-row matrix* is called
a row-vector;
in the second instance, such a *1-column matrix* is called a column-vector. Either
way, these are merely different ways for *storing* vectors,
not different *kinds* of vectors.

In this toolbox, *vectors*,
i.e., lists of numbers, may also end up stored in a 1-row matrix or
in a 1-column matrix, but with the following agreements.

A point in R^{d}, i.e., a d-vector, is always stored
as a column vector. In particular, if you want to supply an `n`

-list
of `d`

-vectors to one of the commands, you are expected
to provide that list as the `n`

columns of a matrix
of size `[d,n]`

.

While other lists of numbers (e.g., a knot sequence or a break sequence) may be stored internally as row vectors, you may supply such lists as you please, as a row vector or a column vector.

Most of the spline commands in this toolbox have names that follow one of the following patterns:

`cs...`

commands construct cubic splines (in
ppform)

`sp...`

commands construct splines in B-form

`fn...`

commands operate on spline functions

`..2...`

commands convert something

`..api`

commands construct an approximation
by interpolation

`..aps`

commands construct an approximation
by smoothing

`..ap2`

commands construct a least-squares
approximation

`...knt`

commands construct (part of) a particular
knot sequence

`...dem`

commands are examples.

For ease of use, most Curve Fitting Toolbox spline functions
have default arguments. In the reference entry under Syntax, we usually
first list the function with all *necessary* input
arguments and then with all *possible* input arguments.
When there is more than one optional argument, then, sometimes, but
not always, their exact order is immaterial. When their order does
matter, you have to specify every optional argument preceding the
one(s) you are interested in. In this situation, you can specify the
default value for an optional argument by using `[]`

(the
empty matrix) as the input for it. The description in the reference
page tells you the default value for each optional input argument.

As in MATLAB, only the output arguments explicitly specified are returned to the user.

MathWorks^{®} would like to acknowledge the contributions
of **Carl de Boor** to the Curve Fitting Toolbox spline
functions. Professor de Boor authored the Spline Toolbox™ from
its first release until Version 3.3.4 (2008).

Professor de Boor received the John von Neumann Prize in 1996
and the National Medal of Science in 2003. He is a member of both
the American Academy of Arts and Sciences and the National Academy
of Sciences. He is the author of *A Practical Guide to Splines* (Springer,
2001).

Some of the spline function naming conventions are the result of a discussion with Jörg Peters, then a graduate student in Computer Sciences at the University of Wisconsin-Madison.