NAG FL Interface
d04aaf (fwd)
1
Purpose
d04aaf calculates a set of derivatives (up to order $14$) of a function of one real variable at a point, together with a corresponding set of error estimates, using an extension of the Neville algorithm.
2
Specification
Fortran Interface
Integer, Intent (In) 
:: 
nder 
Integer, Intent (Inout) 
:: 
ifail 
Real (Kind=nag_wp), External 
:: 
f 
Real (Kind=nag_wp), Intent (In) 
:: 
xval, hbase 
Real (Kind=nag_wp), Intent (Out) 
:: 
der(14), erest(14) 

C Header Interface
#include <nag.h>
void 
d04aaf_ (const double *xval, const Integer *nder, const double *hbase, double der[], double erest[], double (NAG_CALL *f)(const double *x), Integer *ifail) 

C++ Header Interface
#include <nag.h> extern "C" {
void 
d04aaf_ (const double &xval, const Integer &nder, const double &hbase, double der[], double erest[], double (NAG_CALL *f)(const double &x), Integer &ifail) 
}

The routine may be called by the names d04aaf or nagf_numdiff_fwd.
3
Description
d04aaf provides a set of approximations:
to the derivatives:
of a real valued function
$f\left(x\right)$ at a real abscissa
${x}_{0}$, together with a set of error estimates:
which hopefully satisfy:
You must provide the value of
${x}_{0}$, a value of
$n$ (which is reduced to
$14$ should it exceed
$14$), a subroutine which evaluates
$f\left(x\right)$ for all real
$x$, and a step length
$h$. The results
${\mathbf{der}}\left(j\right)$ and
${\mathbf{erest}}\left(j\right)$ are based on
$21$ function values:
Internally
d04aaf calculates the odd order derivatives and the even order derivatives separately. There is an option you can use for restricting the calculation to only odd (or even) order derivatives. For each derivative the routine employs an extension of the Neville Algorithm (see
Lyness and Moler (1969)) to obtain a selection of approximations.
For example, for odd derivatives, based on
$20$ function values,
d04aaf calculates a set of numbers:
each of which is an approximation to
${f}^{\left(2s+1\right)}\left({x}_{0}\right)/\left(2s+1\right)!$. A specific approximation
${T}_{\mathit{k},p,s}$ is of polynomial degree
$2p+2$ and is based on polynomial interpolation using function values
$f\left({x}_{0}\pm \left(2i1\right)h\right)$, for
$\mathit{k}=\mathit{k},\dots ,\mathit{k}+p$. In the absence of roundoff error, the better approximations would be associated with the larger values of
$p$ and of
$k$. However, roundoff error in function values has an increasingly contaminating effect for successively larger values of
$p$. This routine proceeds to make a judicious choice between all the approximations in the following way.
For a specified value of
$s$, let:
where
${U}_{p}={\displaystyle \underset{\mathit{k}}{\mathrm{max}}}\phantom{\rule{0.25em}{0ex}}\left({T}_{\mathit{k},p,s}\right)$ and
${L}_{p}={\displaystyle \underset{\mathit{k}}{\mathrm{min}}}\phantom{\rule{0.25em}{0ex}}\left({T}_{\mathit{k},p,s}\right)$, for
$\mathit{k}=0,1,\dots ,9p$, and let
$\overline{\mathit{p}}$ be such that
${R}_{\overline{\mathit{p}}}={\displaystyle \underset{\mathit{p}}{\mathrm{min}}}\phantom{\rule{0.25em}{0ex}}\left({R}_{\mathit{p}}\right)$, for
$\mathit{p}=s,\dots ,6$.
The routine returns:
and
where
${K}_{j}$ is a safety factor which has been assigned the values:
${K}_{j}=1$, 
$j\le 9$ 
${K}_{j}=1.5$, 
$j=10,11$ 
${K}_{j}=2$ 
$j\ge 12$, 
on the basis of performance statistics.
The even order derivatives are calculated in a precisely analogous manner.
4
References
Lyness J N and Moler C B (1966) van der Monde systems and numerical differentiation Numer. Math. 8 458–464
Lyness J N and Moler C B (1969) Generalised Romberg methods for integrals of derivatives Numer. Math. 14 1–14
5
Arguments

1:
$\mathbf{xval}$ – Real (Kind=nag_wp)
Input

On entry: the point at which the derivatives are required, ${x}_{0}$.

2:
$\mathbf{nder}$ – Integer
Input

On entry: must be set so that its absolute value is the highest order derivative required.
 ${\mathbf{nder}}>0$
 All derivatives up to order $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{nder}},14\right)$ are calculated.
 ${\mathbf{nder}}<0$ and nder is even
 Only even order derivatives up to order $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{nder}},14\right)$ are calculated.
 ${\mathbf{nder}}<0$ and nder is odd
 Only odd order derivatives up to order $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{nder}},13\right)$ are calculated.

3:
$\mathbf{hbase}$ – Real (Kind=nag_wp)
Input

On entry: the initial step length which may be positive or negative. For advice on the choice of
hbase see
Section 9.
Constraint:
${\mathbf{hbase}}\ne 0.0$.

4:
$\mathbf{der}\left(14\right)$ – Real (Kind=nag_wp) array
Output

On exit:
${\mathbf{der}}\left(j\right)$ contains an approximation to the
$j$th derivative of
$f\left(x\right)$ at
$x={\mathbf{xval}}$, so long as the
$j$th derivative is one of those requested by you when specifying
nder. For other values of
$j$,
${\mathbf{der}}\left(j\right)$ is unused.

5:
$\mathbf{erest}\left(14\right)$ – Real (Kind=nag_wp) array
Output

On exit: an estimate of the absolute error in the corresponding result
${\mathbf{der}}\left(j\right)$ so long as the
$j$th derivative is one of those requested by you when specifying
nder. The sign of
${\mathbf{erest}}\left(j\right)$ is positive unless the result
${\mathbf{der}}\left(j\right)$ is questionable. It is set negative when
$\left{\mathbf{der}}\left(j\right)\right<\left{\mathbf{erest}}\left(j\right)\right$ or when for some other reason there is doubt about the validity of the result
${\mathbf{der}}\left(j\right)$ (see
Section 6). For other values of
$j$,
${\mathbf{erest}}\left(j\right)$ is unused.

6:
$\mathbf{f}$ – real (Kind=nag_wp) Function, supplied by the user.
External Procedure

f must evaluate the function
$f\left(x\right)$ at a specified point.
The specification of
f is:
Fortran Interface
Real (Kind=nag_wp) 
:: 
f 
Real (Kind=nag_wp), Intent (In) 
:: 
x 

C Header Interface
double 
f_ (const double *x) 

C++ Header Interface
#include <nag.h> extern "C" {
double 
f_ (const double &x) 
}


1:
$\mathbf{x}$ – Real (Kind=nag_wp)
Input

On entry: the value of the argument
$x$.
If you have equally spaced tabular data, the following information may be useful:

(i)in any call of d04aaf the only values of $x$ for which $f\left(x\right)$ will be required are $x={\mathbf{xval}}$ and
$x={\mathbf{xval}}\pm \left(2\mathit{j}1\right){\mathbf{hbase}}$, for $\mathit{j}=1,2,\dots ,10$; and

(ii)$f\left({x}_{0}\right)$ is always computed, but it is disregarded when only odd order derivatives are required.
f must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which
d04aaf is called. Arguments denoted as
Input must
not be changed by this procedure.
Note: f should not return floatingpoint NaN (Not a Number) or infinity values, since these are not handled by
d04aaf. If your code inadvertently
does return any NaNs or infinities,
d04aaf is likely to produce unexpected results.

7:
$\mathbf{ifail}$ – 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, ${\mathbf{hbase}}=0.0$.
Constraint: ${\mathbf{hbase}}\ne 0.0$.
On entry, ${\mathbf{nder}}=0$.
Constraint: ${\mathbf{nder}}\ne 0$.
 ${\mathbf{ifail}}=99$
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
ifail has a value zero on exit then
d04aaf has terminated successfully, but before any use is made of a derivative
${\mathbf{der}}\left(j\right)$ the value of
${\mathbf{erest}}\left(j\right)$ must be checked.
7
Accuracy
The accuracy of the results is problem dependent. An estimate of the accuracy of each result
${\mathbf{der}}\left(j\right)$ is returned in
${\mathbf{erest}}\left(j\right)$ (see
Sections 3,
5 and
9).
A basic feature of any floatingpoint routine for numerical differentiation based on real function values on the real axis is that successively higher order derivative approximations are successively less accurate. It is expected that in most cases ${\mathbf{der}}\left(14\right)$ will be unusable. As an aid to this process, the sign of ${\mathbf{erest}}\left(j\right)$ is set negative when the estimated absolute error is greater than the approximate derivative itself, i.e., when the approximate derivative may be so inaccurate that it may even have the wrong sign. It is also set negative in some other cases when information available to the routine indicates that the corresponding value of ${\mathbf{der}}\left(j\right)$ is questionable.
The actual values in
erest depend on the accuracy of the function values, the properties of the machine arithmetic, the analytic properties of the function being differentiated and the usersupplied step length
hbase (see
Section 9). The only hard and fast rule is that for a given
${\mathbf{f}}\left({\mathbf{xval}}\right)$ and
hbase, the values of
${\mathbf{erest}}\left(j\right)$ increase with increasing
$j$. The limit of
$14$ is dictated by experience. Only very rarely can one obtain meaningful approximations for higher order derivatives on conventional machines.
8
Parallelism and Performance
d04aaf is not threaded in any implementation.
The time taken by d04aaf depends on the time spent for function evaluations. Otherwise the time is roughly equivalent to that required to evaluate the function $21$ times and calculate a finite difference table having about $200$ entries in total.
The results depend very critically on the choice of the usersupplied step length
hbase. The overall accuracy is diminished as
hbase becomes small (because of the effect of roundoff error) and as
hbase becomes large (because the discretization error also becomes large). If the routine is used four or five times with different values of
hbase one can find a reasonably good value. A process in which the value of
hbase is successively halved (or doubled) is usually quite effective. Experience has shown that in cases in which the Taylor series for
${\mathbf{f}}\left({\mathbf{x}}\right)$ about
xval has a finite radius of convergence
$R$, the choices of
${\mathbf{hbase}}>R/19$ are not likely to lead to good results. In this case some function values lie outside the circle of convergence.
10
Example
This example evaluates the oddorder derivatives of the function:
up to order
$7$ at the point
$x=\frac{1}{2}$. Several different values of
hbase are used, to illustrate that:

(i)extreme choices of hbase, either too large or too small, yield poor results;

(ii)the quality of these results is adequately indicated by the values of erest.
10.1
Program Text
10.2
Program Data
None.
10.3
Program Results