e02agc computes constrained weighted least squares polynomial approximations in Chebyshev series form to an arbitrary set of data points. The values of the approximations and any number of their derivatives can be specified at selected points.

2 Specification

#include <nag.h>

void	e02agc (Nag_OrderType order, Integer m, Integer k, double xmin, double xmax, const double x[], const double y[], const double w[], Integer mf, const double xf[], const double yf[], const Integer p[], double a[], double s[], Integer n, double resid[], NagError fail)

The function may be called by the names: e02agc, nag_fit_dim1_cheb_con or nag_1d_cheb_fit_constr.

3 Description

e02agc determines least squares polynomial approximations of degrees up to

k

to the set of data points

(x_{r}, y_{r})

with weights

w_{r}

, for

r = 1, 2, \dots, m

. The value of

k

, the maximum degree required, is to be prescribed by you. At each of the values

{x f}_{r}

, for

r = 1, 2, \dots, m f

, of the independent variable

x

, the approximations and their derivatives up to order

p_{r}

are constrained to have one of the values

{y f}_{s}

, for

s = 1, 2, \dots, n

, specified by you, where

n = m f + \sum_{r = 0}^{m f} p_{r}

The approximation of degree

i

has the property that, subject to the imposed constraints, it minimizes

σ_{i}

, the sum of the squares of the weighted residuals

ε_{r}

, for

r = 1, 2, \dots, m

, where

ε_{r} = w_{r} (y_{r} - f_{i} (x_{r}))

and

f_{i} (x_{r})

is the value of the polynomial approximation of degree

i

at the

r

th data point.

Each polynomial is represented in Chebyshev series form with normalized argument

\bar{x}

. This argument lies in the range

- 1

+ 1

and is related to the original variable

x

by the linear transformation

\bar{x} = \frac{2 x - (x_{\max} + x_{\min})}{(x_{\max} - x_{\min})}

where

x_{\min}

and

x_{\max}

, specified by you, are respectively the lower and upper end points of the interval of

x

over which the polynomials are to be defined.

The polynomial approximation of degree

i

can be written as

\frac{1}{2} a_{i, 0} + a_{i, 1} T_{1} (\bar{x}) + \dots + a_{i j} T_{j} (\bar{x}) + \dots + a_{i i} T_{i} (\bar{x})

where

T_{j} (\bar{x})

is the Chebyshev polynomial of the first kind of degree

j

with argument

\bar{x}

. For

i = n, n + 1, \dots, k

, the function produces the values of the coefficients

a_{i j}

, for

j = 0, 1, \dots, i

, together with the value of the root mean square residual,

S_{i} = \sqrt{\frac{σ_{i}}{(m^{'} + n - i - 1)}},

where

m^{'}

is the number of data points with nonzero weight.

Values of the approximations may subsequently be computed using e02aec or e02akc.

First e02agc determines a polynomial

μ (\bar{x})

, of degree

n - 1

, which satisfies the given constraints, and a polynomial

ν (\bar{x})

, of degree

n

, which has value (or derivative) zero wherever a constrained value (or derivative) is specified. It then fits

y_{r} - μ (x_{r})

, for

r = 1, 2, \dots, m

, with polynomials of the required degree in

\bar{x}

each with factor

ν (\bar{x})

. Finally the coefficients of

μ (\bar{x})

are added to the coefficients of these fits to give the coefficients of the constrained polynomial approximations to the data points

(x_{r}, y_{r})

, for

r = 1, 2, \dots, m

. The method employed is given in Hayes (1970): it is an extension of Forsythe's orthogonal polynomials method (see Forsythe (1957)) as modified by Clenshaw (see Clenshaw (1960)).

4 References

Clenshaw C W (1960) Curve fitting with a digital computer Comput. J. 2 170–173

Forsythe G E (1957) Generation and use of orthogonal polynomials for data fitting with a digital computer J. Soc. Indust. Appl. Math. 5 74–88

Hayes J G (ed.) (1970) Numerical Approximation to Functions and Data Athlone Press, London

5 Arguments

1: $order$ – Nag_OrderType Input

On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by

order = Nag_RowMajor

. See Section 3.1.3 in the Introduction to the NAG Library CL Interface for a more detailed explanation of the use of this argument.

Constraint:

order = Nag_RowMajor

Nag_ColMajor

2: $m$ – Integer Input

On entry:

m

, the number of data points to be fitted.

Constraint:

m \geq 1

3: $k$ – Integer Input

On entry:

k

, the maximum degree required.

Constraint:

n \leq k \leq m^{''} + n - 1

where

n

is the total number of constraints and

m^{''}

is the number of data points with nonzero weights and distinct abscissae which do not coincide with any of the

{xf}_{r}

4: $xmin$ – double Input

5: $xmax$ – double Input

On entry: the lower and upper end points, respectively, of the interval

[x_{\min}, x_{\max}]

. Unless there are specific reasons to the contrary, it is recommended that xmin and xmax be set respectively to the lowest and highest value among the

x_{r}

and

{x f}_{r}

. This avoids the danger of extrapolation provided there is a constraint point or data point with nonzero weight at each end point.

Constraint:

xmax > xmin

6: $x [m]$ – const double Input

On entry:

x [r - 1]

must contain the value

x_{r}

of the independent variable at the

r

th data point, for

r = 1, 2, \dots, m

Constraint: the

x [r - 1]

must be in nondecreasing order and satisfy

xmin \leq x [r - 1] \leq xmax

7: $y [m]$ – const double Input

On entry:

y [r - 1]

must contain

y_{r}

, the value of the dependent variable at the

r

th data point, for

r = 1, 2, \dots, m

8: $w [m]$ – const double Input

On entry:

w [r - 1]

must contain the weight

w_{r}

to be applied to the data point

x_{r}

, for

r = 1, 2, \dots, m

. For advice on the choice of weights see the E02 Chapter Introduction. Negative weights are treated as positive. A zero weight causes the corresponding data point to be ignored. Zero weight should be given to any data point whose

x

and

y

values both coincide with those of a constraint (otherwise the denominators involved in the root mean square residuals

S_{i}

will be slightly in error).

9: $mf$ – Integer Input

On entry:

m f

, the number of values of the independent variable at which a constraint is specified.

Constraint:

mf \geq 1

10: $xf [mf]$ – const double Input

On entry:

xf [r - 1]

must contain

{x f}_{r}

, the value of the independent variable at which a constraint is specified, for

r = 1, 2, \dots, mf

Constraint: these values need not be ordered but must be distinct and satisfy

xmin \leq xf [r - 1] \leq xmax

11: $yf [\dim]$ – const double Input

Note: the dimension, dim, of the array yf must be at least

(mf + \sum_{i = 0}^{mf - 1} p [i])

On entry: the values which the approximating polynomials and their derivatives are required to take at the points specified in xf. For each value of

xf [r - 1]

, yf contains in successive elements the required value of the approximation, its first derivative, second derivative,

\dots, p_{r}

th derivative, for

r = 1, 2, \dots, m f

. Thus the value,

{y f}_{s}

, which the

k

th derivative of each approximation (

k = 0

referring to the approximation itself) is required to take at the point

xf [r - 1]

must be contained in

yf [s - 1]

, where

s = r + k + p_{1} + p_{2} + \dots + p_{r - 1},

where

k = 0, 1, \dots, p_{r}

and

r = 1, 2, \dots, m f

. The derivatives are with respect to the independent variable

x

12: $p [mf]$ – const Integer Input

On entry:

p [r - 1]

must contain

p_{r}

, the order of the highest-order derivative specified at

xf [r - 1]

, for

r = 1, 2, \dots, m f

p_{r} = 0

implies that the value of the approximation at

xf [r - 1]

is specified, but not that of any derivative.

Constraint:

p [r - 1] \geq 0

, for

r = 1, 2, \dots, mf

13: $a [\dim]$ – double Output

Note: the dimension, dim, of the array a must be at least

(k + 1, k + 1) \times (k + 1)

the dimension, dim, of the array a must be at least

(k + 1) \times (k + 1, k + 1)

where

A (i, j)

appears in this document, it refers to the array element

$a [(j - 1) \times ((k + 1, k + 1)) + i - 1]$ when $order = Nag_ColMajor$ ;
$a [(i - 1) \times (k + 1) + j - 1]$ when $order = Nag_RowMajor$ .

On exit:

A (i + 1, j + 1)

contains the coefficient

a_{i j}

in the approximating polynomial of degree

i

, for

i = n, \dots, k

and

j = 0, 1, \dots, i

14: $s [k + 1]$ – double Output

On exit:

s [i]

contains

S_{i}

, for

i = n, \dots, k

, the root mean square residual corresponding to the approximating polynomial of degree

i

. In the case where the number of data points with nonzero weight is equal to

k + 1 - n

S_{i}

is indeterminate: the function sets it to zero. For the interpretation of the values of

S_{i}

and their use in selecting an appropriate degree, see Section 3.1 in the E02 Chapter Introduction.

15: $n$ – Integer * Output

On exit: contains the total number of constraint conditions imposed:

n = mf + p_{1} + p_{2} + \dots + p_{mf}

16: $resid [m]$ – double Output

On exit: contains weighted residuals of the highest degree of fit determined

(k)

. The residual at

x_{r}

is in element

resid [r - 1]

, for

r = 1, 2, \dots, m

17: $fail$ – NagError * Input/Output

The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM: On entry, argument $〈value〉$ had an illegal value.
NE_CONSTRAINT: Constraint: $n \leq k \leq m^{''} + n - 1$ where $n$ is the total number of constraints and $m^{''}$ is the number of data points with nonzero weights and distinct abscissae which do not coincide with any of the ${xf}_{r}$ .
NE_ILL_CONDITIONED: The polynomials $μ (x)$ and/or $ν (x)$ cannot be found. The problem is too ill-conditioned. This may occur when the constraint points are very close together, or large in number, or when an attempt is made to constrain high-order derivatives.
NE_INT: On entry, $m = 〈value〉$ .
Constraint: $m \geq 1$ .

On entry, $mf = 〈value〉$ .
Constraint: $mf \geq 1$ .
NE_INT_3: On entry, $k + 1 > m^{''} + n$ , where $m^{''}$ is the number of data points with nonzero weight and distinct abscissae different from all xf, and n is the total number of constraints: $k + 1 = 〈value〉$ , $m^{''} = 〈value〉$ and $n = 〈value〉$ .
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.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_NOT_MONOTONIC: On entry, $i = 〈value〉$ , $x [i - 1] = 〈value〉$ and $x [i - 2] = 〈value〉$ .
Constraint: $x [i - 1] \geq x [i - 2]$ .
NE_REAL_2: On entry, $xmin = 〈value〉$ and $xmax = 〈value〉$ .
Constraint: $xmin < xmax$ .
NE_REAL_ARRAY: On entry, $I = 〈value〉$ , $xf [I - 1] = 〈value〉$ , $J = 〈value〉$ and $xf [J - 1] = 〈value〉$ .
Constraint: $xf [I - 1] \neq xf [J - 1]$ .

On entry, $xf [I - 1]$ lies outside interval $[xmin, xmax]$ : $I = 〈value〉$ , $xf [I - 1] = 〈value〉$ , $xmin = 〈value〉$ and $xmax = 〈value〉$ .

On entry, $x [I - 1]$ lies outside interval $[xmin, xmax]$ : $I = 〈value〉$ , $x [I - 1] = 〈value〉$ , $xmin = 〈value〉$ and $xmax = 〈value〉$ .

On entry, $x [I - 1]$ lies outside interval $[xmin, xmax]$ for some $I$ .

7 Accuracy

No complete error analysis exists for either the interpolating algorithm or the approximating algorithm. However, considerable experience with the approximating algorithm shows that it is generally extremely satisfactory. Also the moderate number of constraints, of low-order, which are typical of data fitting applications, are unlikely to cause difficulty with the interpolating function.

8 Parallelism and Performance

e02agc is not threaded in any implementation.

9 Further Comments

The time taken to form the interpolating polynomial is approximately proportional to

n^{3}

, and that to form the approximating polynomials is very approximately proportional to

m (k + 1) (k + 1 - n)

To carry out a least squares polynomial fit without constraints, use e02adc. To carry out polynomial interpolation only, use e01aec.

10 Example

This example reads data in the following order, using the notation of the argument list above:

mf
$p [i - 1]$ , $xf [i - 1]$ , Y-value and derivative values (if any) at $xf [i - 1]$ , for $i = 1, 2, \dots, mf$
m
$x [i - 1]$ , $y [i - 1]$ , $w [i - 1]$ , for $i = 1, 2, \dots, m$
k, xmin, xmax

The output is:

the root mean square residual for each degree from $n$ to $k$ ;
the Chebyshev coefficients for the fit of degree $k$ ;
the data points, and the fitted values and residuals for the fit of degree $k$ .

The program is written in a generalized form which will read any number of datasets.

The dataset supplied specifies

5

data points in the interval

[0.0, 4.0]

with unit weights, to which are to be fitted polynomials,

p

, of degrees up to

4

, subject to the

3

constraints:

$p (0.0) = 1.0, p^{'} (0.0) = - 2.0, p (4.0) = 9.0 .$

Interfaces: FL CL AD

NAG CL Interface Introduction

E02 (Fit) Chapter Contents

E02 (Fit) Chapter Introduction

e02ag: FL CL AD

NAG CL Interfacee02agc (dim1_​cheb_​con)

▸▿ 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 CL Interface
e02agc (dim1_cheb_con)