Integer, Intent (In)	::	m, n, sda, lda
Integer, Intent (Inout)	::	ifail
Integer, Intent (Out)	::	irank, iter
Real (Kind=nag_wp), Intent (In)	::	tol
Real (Kind=nag_wp), Intent (Inout)	::	a(lda,sda), b(m), relerr
Real (Kind=nag_wp), Intent (Out)	::	x(n), resmax

C Header Interface

#include <nag.h>

void	e02gcf_ (const Integer m, const Integer n, const Integer sda, const Integer lda, double a[], double b[], const double tol, double relerr, double x[], double resmax, Integer irank, Integer iter, Integer ifail)

The routine may be called by the names e02gcf or nagf_fit_glin_linf.

3 Description

Given a matrix

A

with

m

rows and

n

columns

(m \geq n)

and a vector

b

with

m

elements, the routine calculates an

l_{\infty}

solution to the overdetermined system of equations

A x = b .

That is to say, it calculates a vector

x

, with

n

elements, which minimizes the

l_{\infty}

norm of the residuals (the absolutely largest residual)

r (x) = \max_{1 \leq i \leq m} | r_{i} |

where the residuals

r_{i}

are given by

r_{i} = b_{i} - \sum_{j = 1}^{n} a_{i j} x_{j}, i = 1, 2, \dots, m .

Here

a_{i j}

is the element in row

i

and column

j

A

b_{i}

is the

i

th element of

b

and

x_{j}

the

j

th element of

x

. The matrix

A

need not be of full rank. The solution is not unique in this case, and may not be unique even if

A

is of full rank.

Alternatively, in applications where a complete minimization of the

l_{\infty}

norm is not necessary, you may obtain an approximate solution, usually in shorter time, by giving an appropriate value to the argument relerr.

Typically in applications to data fitting, data consisting of

m

points with coordinates

(t_{i}, y_{i})

is to be approximated in the

l_{\infty}

norm by a linear combination of known functions

ϕ_{j} (t)

α_{1} ϕ_{1} (t) + α_{2} ϕ_{2} (t) + \dots + α_{n} ϕ_{n} (t) .

This is equivalent to finding an

l_{\infty}

solution to the overdetermined system of equations

\sum_{j = 1}^{n} ϕ_{j} (t_{i}) α_{j} = y_{i}, i = 1, 2, \dots, m .

Thus if, for each value of

i

and

j

the element

a_{i j}

of the matrix

A

above is set equal to the value of

ϕ_{j} (t_{i})

and

b_{i}

is set equal to

y_{i}

, the solution vector

x

will contain the required values of the

α_{j}

. Note that the independent variable

t

above can, instead, be a vector of several independent variables (this includes the case where each

ϕ_{i}

is a function of a different variable, or set of variables).

The algorithm is a modification of the simplex method of linear programming applied to the dual formation of the

l_{\infty}

problem (see Barrodale and Phillips (1974) and Barrodale and Phillips (1975)). The modifications are designed to improve the efficiency and stability of the simplex method for this particular application.

4 References

Barrodale I and Phillips C (1974) An improved algorithm for discrete Chebyshev linear approximation Proc. 4th Manitoba Conf. Numerical Mathematics 177–190 University of Manitoba, Canada

Barrodale I and Phillips C (1975) Solution of an overdetermined system of linear equations in the Chebyshev norm [F4] (Algorithm 495) ACM Trans. Math. Software 1(3) 264–270

5 Arguments

1: $m$ – Integer Input: On entry: the number of equations, $m$ (the number of rows of the matrix $A$ ).

Constraint: $m \geq n$ .
2: $n$ – Integer Input: On entry: the number of unknowns, $n$ (the number of columns of the matrix $A$ ).

Constraint: $n \geq 1$ .
3: $sda$ – Integer Input: On entry: the second dimension of the array a as declared in the (sub)program from which e02gcf is called.

Constraint: $sda \geq m + 1$ .
4: $lda$ – Integer Input: On entry: the first dimension of the array a as declared in the (sub)program from which e02gcf is called.

Constraint: $lda \geq n + 3$ .
5: $a (lda, sda)$ – Real (Kind=nag_wp) array Input/Output: On entry: $a (j, i)$ must contain $a_{i j}$ , the element in the $i$ th row and $j$ th column of the matrix $A$ , for $i = 1, 2, \dots, m$ and $j = 1, 2, \dots, n$ , (that is, the transpose of the matrix). The remaining elements need not be set. Preferably, the columns of the matrix $A$ (rows of the argument a) should be scaled before entry: see Section 7.

On exit: contains the last simplex tableau.
6: $b (m)$ – Real (Kind=nag_wp) array Input/Output: On entry: $b (i)$ must contain $b_{i}$ , the $i$ th element of the vector $b$ , for $i = 1, 2, \dots, m$ .

On exit: the $i$ th residual $r_{i}$ corresponding to the solution vector $x$ , for $i = 1, 2, \dots, m$ . Note however that these residuals may contain few significant figures, especially when resmax is within one or two orders of magnitude of tol. Indeed if $resmax \leq tol$ , the elements $b (i)$ may all be set to zero. It is, therefore, often advisable to compute the residuals directly.
7: $tol$ – Real (Kind=nag_wp) Input: On entry: a threshold below which numbers are regarded as zero. The recommended threshold value is $10.0 \times ε$ , where $ε$ is the machine precision. If $tol \leq 0.0$ on entry, the recommended value is used within the routine. If premature termination occurs, a larger value for tol may result in a valid solution.

Suggested value: $0.0$ .
8: $relerr$ – Real (Kind=nag_wp) Input/Output: On entry: must be set to a bound on the relative error acceptable in the maximum residual at the solution.
If $relerr \leq 0.0$ , the $l_{\infty}$ solution is computed, and relerr is set to $0.0$ on exit.

If $relerr > 0.0$ , the routine obtains instead an approximate solution for which the largest residual is less than $1.0 + relerr$ times that of the $l_{\infty}$ solution; on exit, relerr contains a smaller value such that the above bound still applies. (The usual result of this option, say with $relerr = 0.1$ , is a saving in the number of simplex iterations).

On exit: is altered as described above.
9: $x (n)$ – Real (Kind=nag_wp) array Output: On exit: if $ifail = 0$ or $1$ , $x (j)$ contains the $j$ th element of the solution vector $x$ , for $j = 1, 2, \dots, n$ . Whether this is an $l_{\infty}$ solution or an approximation to one, depends on the value of relerr on entry.
10: $resmax$ – Real (Kind=nag_wp) Output: On exit: if $ifail = 0$ or $1$ , resmax contains the absolute value of the largest residual(s) for the solution vector $x$ . (See b.)
11: $irank$ – Integer Output: On exit: if $ifail = 0$ or $1$ , irank contains the computed rank of the matrix $A$ .
12: $iter$ – Integer Output: On exit: if $ifail = 0$ or $1$ , iter contains the number of iterations taken by the simplex method.
13: $ifail$ – Integer Input/Output: On entry: ifail must be set to $0$ , $−1$ or $1$ to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of $0$ causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of $−1$ means that an error message is printed while a value of $1$ means that it is not.

If halting is not appropriate, the value $−1$ or $1$ is recommended. If message printing is undesirable, then the value $1$ is recommended. Otherwise, the value $−1$ is recommended since useful values can be provided in some output arguments even when $ifail \neq 0$ on exit. 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).

6 Error Indicators and Warnings

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:

Note: in some cases e02gcf may return useful information.

$ifail = 1$: An optimal solution has been obtained, but may not be unique.

$ifail = 2$: Premature termination due to rounding errors. Try using larger value of tol: $tol = ⟨ value ⟩$ .

$ifail = 3$: On entry, $lda = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $lda \geq n + 3$ .

On entry, $m = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $m \geq n$ .

On entry, $n = ⟨ value ⟩$ .
Constraint: $n \geq 1$ .

On entry, $sda = ⟨ value ⟩$ and $m = ⟨ value ⟩$ .
Constraint: $sda \geq m + 1$ .

$ifail = - 99$: 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.

$ifail = - 399$: 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.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.

7 Accuracy

Experience suggests that the computational accuracy of the solution

x

is comparable with the accuracy that could be obtained by applying Gaussian elimination with partial pivoting to the

n + 1

equations which have residuals of largest absolute value. The accuracy, therefore, varies with the conditioning of the problem, but has been found generally very satisfactory in practice.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

e02gcf is not threaded in any implementation.

9 Further Comments

The effects of

m

and

n

on the time and on the number of iterations in the simplex method vary from problem to problem, but typically the number of iterations is a small multiple of

n

and the total time is approximately proportional to

m n^{2}

It is recommended that, before the routine is entered, the columns of the matrix

A

are scaled so that the largest element in each column is of the order of unity. This should improve the conditioning of the matrix, and also enable the argument tol to perform its correct function. The solution

x

obtained will then, of course, relate to the scaled form of the matrix. Thus if the scaling is such that, for each

j = 1, 2, \dots, n

, the elements of the

j

th column are multiplied by the constant

k_{j}

, the element

x_{j}

of the solution vector

x

must be multiplied by

k_{j}

if it is desired to recover the solution corresponding to the original matrix

A

10 Example

This example approximates a set of data by a curve of the form

y = K e^{t} + L e^{- t} + M

where

K

L

and

M

are unknown. Given values

y_{i}

5

points

t_{i}

we may form the overdetermined set of equations for

K

L

and

M

e^{t_{i}} K + e^{- t_{i}} L + M = y_{i}, i = 1, 2, \dots, 5 .

e02gcf is used to solve these in the

l_{\infty}

sense.

e02gc: FL CL CPP AD PY MB

NAG FL Interfacee02gcf (glin_​linf)

▸▿ 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
e02gcf (glin_linf)