e01bff (dim1_monotonic_eval) : NAG Library, Mark 27

e01bff evaluates a piecewise cubic Hermite interpolant at a set of points.

The routine may be called by the names e01bff or nagf_interp_dim1_monotonic_eval.

e01bff evaluates a piecewise cubic Hermite interpolant, as computed by e01bef, at the points

px (i)

, for

i = 1, 2, \dots, m

. If any point lies outside the interval from

x (1)

x (n)

, a value is extrapolated from the nearest extreme cubic, and a warning is returned.

The routine is derived from routine PCHFE in Fritsch (1982).

Fritsch F N (1982) PCHIP final specifications Report UCID-30194 Lawrence Livermore National Laboratory

On entry: n, x, f and d must be unchanged from the previous call of e01bef.

On entry:

m

, the number of points at which the interpolant is to be evaluated. If any point lies outside the interval from

x (1)

) to

x (n)

, a value is extrapolated from the nearest extreme cubic, and a warning is returned. The extrapolation simply extends the final cubic at each end.

Constraint:

m \geq 1

On entry: the

m

values of

x

at which the interpolant is to be evaluated.

On exit:

pf (i)

contains the value of the interpolant evaluated at the point

px (i)

, for

i = 1, 2, \dots, m

On entry: ifail must be set to

0

- 1 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 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 $- 1 or 1$ is used it is essential to test the value of ifail on exit.

On exit:

ifail = 0

unless the routine detects an error or a warning has been flagged (see Section 6).

If on entry

ifail = 0

- 1

, explanatory error messages are output on the current error message unit (as defined by x04aaf).

Errors or warnings detected by the routine:

On entry,

n = 〈value〉

.
Constraint:

n \geq 2

On entry,

r = 〈value〉

x (r - 1) = 〈value〉

and

x (r) = 〈value〉

.
Constraint:

x (r - 1) < x (r)

for all

r

On entry,

m = 〈value〉

.
Constraint:

m \geq 1

Warning – some points in array px lie outside the range

x (1) \dots x (n)

. Values at these points are unreliable because computed by extrapolation.

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.

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.

Dynamic memory allocation failed.

See Section 9 in the Introduction to the NAG Library FL Interface for further information.

The computational errors in the array pf should be negligible in most practical situations.

e01bff is not threaded in any implementation.

The time taken by e01bff is approximately proportional to the number of evaluation points,

m

. The evaluation will be most efficient if the elements of px are in nondecreasing order (or, more generally, if they are grouped in increasing order of the intervals

[x (r - 1), x (r)]

). A single call of e01bff with

m > 1

is more efficient than several calls with

m = 1

As documented above, this routine will use extrapolation if presented with evaluation points outside the region

[x_{1}, x_{n}]

. Since such extrapolated values are computed simply by extending the cubic approximation at each end interval, the values may not be suitable for all purposes. If you need more control over how values outside the original region are calculated, consider the following possible procedures for degree

0

1

and

2

extrapolation.

(i) Flat extrapolation
- For $x < x_{1}$ choose $f = f (1)$ ;
- for $x > x_{n}$ choose $f = f (n)$ .
(ii) Linear extrapolation
- For $x < x_{1}$ , call e01bgf using $px (1) = x (1)$ to obtain $pd (1)$ , then choose $f = pd (1) \times (x - x (1)) + f (1)$ ;
- for $x > x_{n}$ , call e01bgf using $px (1) = x (n)$ to obtain $pd (n)$ , then choose $f = pd (n) \times (x - x (n)) + f (n)$ .
(iii) Quadratic extrapolation
- For $x < x_{1}$ , call e01bgf to obtain derivative values $pd (1)$ and $pd (2)$ at $x_{1}$ and $x_{2}$ ,
  - if $|pd (2)| \geq |pd (1)|$ revert to linear extrapolation (ii),
  - otherwise let $l_{1} (x) = (x - x_{1}), l_{2} (x) = (x - x_{2})$ and $c = 1 / (x_{1} - x_{2})$ , then choose $f (x) = pd (1) \times l_{1} (x) \times l_{2} (x) \times c - f (1) \times l_{2} (x) \times (l_{1} (x) + l_{1} (x_{2})) \times c^{2} + f (2) \times {[l_{1} (x) \times c]}^{2}$ ;
- for $x > x_{n}$ , call e01bgf to obtain derivative values $pd (n)$ and $pd (n - 1)$ at $x_{n}$ and $x_{n - 1}$ ,
  - if $|pd (n - 1)| \geq |pd (n)|$ revert to linear extrapolation (ii),
  - otherwise let $l_{n} (x) = (x - x_{n}), l_{n - 1} (x) = (x - x_{n - 1})$ and $c = 1 / (x_{n} - x_{n - 1})$ , then choose $f (x) = pd (n) \times l_{n} (x) \times l_{n - 1} (x) \times c - f (n) \times l_{n - 1} (x) \times (l_{n} (x) + l_{n} (x_{n - 1})) \times c^{2} + f (n - 1) \times {[l_{n} (x) \times c]}^{2}$ .

This example reads in values of n, x, f and d, and then calls e01bff to evaluate the interpolant at equally spaced points.

NAG FL Interface
e01bff (dim1_monotonic_eval)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG FL Interfacee01bff (dim1_​monotonic_​eval)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG FL Interface
e01bff (dim1_monotonic_eval)