NAG Library Function Document
nag_1d_cheb_interp (e01aec)
1 Purpose
nag_1d_cheb_interp (e01aec) constructs the Chebyshev series representation of a polynomial interpolant to a set of data which may contain derivative values.
2 Specification
#include <nag.h> |
#include <nage01.h> |
void |
nag_1d_cheb_interp (Integer m,
double xmin,
double xmax,
const double x[],
const double y[],
const Integer p[],
Integer itmin,
Integer itmax,
double a[],
double perf[],
Integer *num_iter,
NagError *fail) |
|
3 Description
Let distinct values of an independent variable be given, with , for . For each value , suppose that the value of the dependent variable together with the first derivatives of with respect to are given. Each must therefore be a non-negative integer, with the total number of interpolating conditions, , equal to .
nag_1d_cheb_interp (e01aec) calculates the unique polynomial
of degree
(or less) which is such that
, for
and
. Here
means
. This polynomial is represented in Chebyshev series form in the normalized variable
, as follows:
where
so that
for
in the interval
to
, and where
is the Chebyshev polynomial of the first kind of degree
with argument
.
(The polynomial interpolant can subsequently be evaluated for any value of
in the given range by using
nag_1d_cheb_eval2 (e02akc). Chebyshev series representations of the derivative(s) and integral(s) of
may be obtained by (repeated) use of
nag_1d_cheb_deriv (e02ahc) and
nag_1d_cheb_intg (e02ajc).)
The method used consists first of constructing a divided-difference table from the normalized
values and the given values of
and its derivatives with respect to
. The Newton form of
is then obtained from this table, as described in
Huddleston (1974) and
Krogh (1970), with the modification described in
Section 9.2. The Newton form of the polynomial is then converted to Chebyshev series form as described in
Section 9.3.
Since the errors incurred by these stages can be considerable, a form of iterative refinement is used to improve the solution. This refinement is particularly useful when derivatives of rather high order are given in the data. In reasonable examples, the refinement will usually terminate with a certain accuracy criterion satisfied by the polynomial (see
Section 7). In more difficult examples, the criterion may not be satisfied and refinement will continue until the maximum number of iterations (as specified by the input argument
itmax) is reached.
In extreme examples, the iterative process may diverge (even though the accuracy criterion is satisfied): if a certain divergence criterion is satisfied, the process terminates at once. In all cases the function returns the ‘best’ polynomial achieved before termination. For the definition of ‘best’ and details of iterative refinement and termination criteria, see
Section 9.4.
4 References
Huddleston R E (1974) CDC 6600 routines for the interpolation of data and of data with derivatives SLL-74-0214 Sandia Laboratories (Reprint)
Krogh F T (1970) Efficient algorithms for polynomial interpolation and numerical differentiation Math. Comput. 24 185–190
5 Arguments
- 1:
m – IntegerInput
On entry: , the number of given values of the independent variable .
Constraint:
.
- 2:
xmin – doubleInput
- 3:
xmax – doubleInput
On entry: the lower and upper end points, respectively, of the interval . If they are not determined by your problem, it is recommended that they be set respectively to the smallest and largest values among the .
Constraint:
.
- 4:
x[m] – const doubleInput
On entry: must be set to the value of , for . The need not be ordered.
Constraint:
, and the must be distinct.
- 5:
y[] – const doubleInput
-
Note: the dimension,
dim, of the array
y
must be at least
.
On entry: the given values of the dependent variable, and derivatives, as follows:
The first elements contain in that order.
The next elements contain in that order.
The last elements contain in that order.
- 6:
p[m] – const IntegerInput
On entry: must be set to , the order of the highest-order derivative whose value is given at , for . If the value of only is given for some then the corresponding value of must be zero.
Constraint:
, for .
- 7:
itmin – IntegerInput
- 8:
itmax – IntegerInput
On entry: respectively the minimum and maximum number of iterations to be performed by the function (for full details see
Section 9.4). Setting
itmin and/or
itmax negative or zero invokes default value(s) of
and/or
, respectively.
The default values will be satisfactory for most problems, but occasionally significant improvement will result from using higher values.
Suggested value:
and .
- 9:
a[] – doubleOutput
-
Note: the dimension,
dim, of the array
a
must be at least
.
On exit: contains the coefficient in the Chebyshev series representation of , for .
- 10:
perf[] – doubleOutput
-
Note: the dimension,
dim, of the array
perf
must be at least
.
On exit:
, for
, contains the ratio of
, the performance index relating to the
th derivative of the
finally provided, to
times the
machine precision.
, for
, contains the
th residual, i.e., the value of
, where
and
are the appropriate values corresponding to the
th element in the array
y (see the description of
y in
Section 5).
This information is also output if
NE_ITER_FAIL or
NE_NOT_ACC.
- 11:
num_iter – Integer *Output
On exit:
num_iter contains the number of iterations actually performed in deriving
.
This information is also output if
NE_ITER_FAIL or
NE_NOT_ACC.
- 12:
fail – NagError *Input/Output
-
The NAG error argument (see
Section 3.6 in the Essential Introduction).
6 Error Indicators and Warnings
- NE_ALLOC_FAIL
-
Dynamic memory allocation failed.
- NE_BAD_PARAM
-
On entry, argument had an illegal value.
- NE_INT
-
On entry, .
Constraint: .
- NE_INT_ARRAY
-
On entry, and .
Constraint: , for .
- NE_INTERNAL_ERROR
-
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact
NAG for assistance.
- NE_ITER_FAIL
-
Iteration is divergent. Problem is ill-conditioned.
- NE_NOT_ACC
-
Not all performance indices are small enough. Try increasing
itmax:
.
- NE_REAL_2
-
On entry, and .
Constraint: .
- NE_REAL_ARRAY
-
On entry, , and .
Constraint: .
On entry, , , and .
Constraint: .
7 Accuracy
A complete error analysis is not currently available, but the method gives good results for reasonable problems.
It is important to realise that for some sets of data, the polynomial interpolation problem is ill-conditioned. That is, a small perturbation in the data may induce large changes in the polynomial, even in exact arithmetic. Though by no means the worst example, interpolation by a single polynomial to a large number of function values given at points equally spaced across the range is notoriously ill-conditioned and the polynomial interpolating such a dataset is prone to exhibit enormous oscillations between the data points, especially near the ends of the range. These will be reflected in the Chebyshev coefficients being large compared with the given function values. A more familiar example of ill-conditioning occurs in the solution of certain systems of linear algebraic equations, in which a small change in the elements of the matrix and/or in the components of the right-hand side vector induces a relatively large change in the solution vector. The best that can be achieved in these cases is to make the residual vector small in some sense. If this is possible, the computed solution is exact for a slightly perturbed set of data. Similar considerations apply to the interpolation problem.
The residuals
are available for inspection
.
To assess whether these are reasonable, however, it is necessary to relate them to the largest function and derivative values taken by
over the interval
. The following performance indices aim to do this. Let the
th derivative of
with respect to the normalized variable
be given by the Chebyshev series
Let
denote the sum of the moduli of these coefficients (this is an upper bound on the
th derivative in the interval and is taken as a measure of the maximum size of this derivative), and define
Then if the root-mean-square value of the residuals of
, scaled so as to relate to the normalized variable
, is denoted by
, the performance indices are defined by
It is expected that, in reasonable cases, they will all be less than (say)
times the
machine precision (this is the accuracy criterion mentioned in
Section 3), and in many cases will be of the order of
machine precision or less.
8 Parallelism and Performance
Not applicable.
Computation time is approximately proportional to , where is the number of iterations actually used.
In constructing each new coefficient in the Newton form of the polynomial, a new must be brought into the computation. The chosen is that which yields the smallest new coefficient. This strategy increases the stability of the divided-difference technique, sometimes quite markedly, by reducing errors due to cancellation.
Conversion from the Newton form to Chebyshev series form is effected by evaluating the former at the
values of
at which
takes the value
, and then interpolating these
function values by a call of
nag_1d_cheb_interp_fit (e02afc), which provides the Chebyshev series representation of the polynomial with very small additional relative error.
The iterative refinement process is performed as follows.
Firstly, an initial approximation,
say, is found by the technique described in
Section 3. The
th step of the refinement process then consists of evaluating the residuals of the
th approximation
, and constructing an interpolant,
, to these residuals. The next approximation
to the interpolating polynomial is then obtained as
This completes the description of the
th step.
The iterative process is terminated according to the following criteria. When a polynomial is found whose performance indices (as defined in
Section 7) are all less than
times the
machine precision, the process terminates after
itmin further iterations (or after a total of
itmax iterations if that occurs earlier). This will occur in most reasonable problems. The extra iterations are to allow for the possibility of further improvement. If no such polynomial is found, the process terminates after a total of
itmax iterations. Both these criteria are over-ridden, however, in two special cases. Firstly, if for some value of
the sum of the moduli of the Chebyshev coefficients of
is greater than that of
, it is concluded that the process is diverging and the process is terminated at once (
is not computed).
Secondly, if at any stage, the performance indices are all computed as zero, again the process is terminated at once.
As the iterations proceed, a record is kept of the best polynomial. Subsequently, at the end of each iteration, the new polynomial replaces the current best polynomial if it satisfies two conditions (otherwise the best polynomial remains unchanged). The first condition is that at least one of its root-mean-square residual values,
(see
Section 7) is smaller than the corresponding value for the current best polynomial. The second condition takes two different forms according to whether or not the performance indices (see
Section 7) of the current best polynomial are all less than
times the
machine precision. If they are, then the largest performance index of the new polynomial is required to be less than that of the current best polynomial. If they are not, the number of indices which are less than
times the
machine precision must not be smaller than for the current best polynomial. When the iterative process is terminated, it is the polynomial then recorded as best, which is returned to you as
.
10 Example
This example constructs an interpolant
to the following data:
The coefficients in the Chebyshev series representation of
are printed, and also the residuals corresponding to each of the given function and derivative values.
This program is written in a generalized form which can read any number of data-sets.
10.1 Program Text
Program Text (e01aece.c)
10.2 Program Data
Program Data (e01aece.d)
10.3 Program Results
Program Results (e01aece.r)