NAG Library Routine Document

e04fcf (lsq_uncon_mod_func_comp)

1

Purpose

2

Specification

3

Description

4

References

5

Arguments

1:     $\mathbf{m}$ – IntegerInput

2:     $\mathbf{n}$ – IntegerInput

On entry: the number $m$ of residuals, $f_i\left(x\right)$, and the number $n$ of variables, $x_j$.

Constraint: $1\le \mathbf{n}\le \mathbf{m}$.

3:     $\mathbf{lsqfun}$ – Subroutine, supplied by the user.External Procedure

lsqfun must calculate the vector of values $f_i\left(x\right)$ at any point $x$. (However, if you do not wish to calculate the residuals at a particular $x$, there is the option of setting an argument to cause e04fcf to terminate immediately.)

The specification of lsqfun is:

Fortran Interface

Subroutine lsqfun ( iflag, m, n, xc, fvec, iw, liw, w, lw) Integer, Intent (In) :: m, n, liw, lw Integer, Intent (Inout) :: iflag, iw(liw) Real (Kind=nag_wp), Intent (In) :: xc(n) Real (Kind=nag_wp), Intent (Inout) :: w(lw) Real (Kind=nag_wp), Intent (Out) :: fvec(m)

C Header Interface

#include <nagmk26.h> void  lsqfun (Integer *iflag, const Integer *m, const Integer *n, const double xc[], double fvec[], Integer iw[], const Integer *liw, double w[], const Integer *lw)

1:     $\mathbf{iflag}$ – IntegerInput/Output

On entry: has a non-negative value.

On exit: if lsqfun resets iflag to some negative number, e04fcf will terminate immediately, with ifail set to your setting of iflag.

2:     $\mathbf{m}$ – IntegerInput

On entry: $m$, the numbers of residuals.

3:     $\mathbf{n}$ – IntegerInput

On entry: $n$, the numbers of variables.

4:     $\mathbf{xc}\left(\mathbf{n}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: the point $x$ at which the values of the $f_i$ are required.

5:     $\mathbf{fvec}\left(\mathbf{m}\right)$ – Real (Kind=nag_wp) arrayOutput

On exit: unless iflag is reset to a negative number, $\mathbf{fvec}\left(\mathit{i}\right)$ must contain the value of $f_{\mathit{i}}$ at the point $x$, for $\mathit{i}=1,2,\dots ,m$.

6:     $\mathbf{iw}\left(\mathbf{liw}\right)$ – Integer arrayWorkspace

7:     $\mathbf{liw}$ – IntegerInput

8:     $\mathbf{w}\left(\mathbf{lw}\right)$ – Real (Kind=nag_wp) arrayWorkspace

9:     $\mathbf{lw}$ – IntegerInput

lsqfun is called with these arguments as in the call to e04fcf, so you can pass quantities to lsqfun from the subroutine which calls e04fcf by using partitions of iw and w beyond those used as workspace by e04fcf. However, because of the danger of mistakes in partitioning, it is recommended that this facility be used very selectively, e.g., for stable applications packages which need to pass their own variable dimension workspace to lsqfun. It is recommended that the normal method for passing information from your subroutine to lsqfun should be via COMMON global variables. In any case, you must not change liw, lw or the elements of iw and w used as workspace by e04fcf.

lsqfun must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which e04fcf is called. Arguments denoted as Input must not be changed by this procedure.

Note: lsqfun should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by e04fcf. If your code inadvertently does return any NaNs or infinities, e04fcf is likely to produce unexpected results.

lsqfun should be tested separately before being used in conjunction with e04fcf.

4:     $\mathbf{lsqmon}$ – Subroutine, supplied by the NAG Library or the user.External Procedure

If $\mathbf{iprint}\ge 0$, you must supply lsqmon which is suitable for monitoring the minimization process. lsqmon must not change the values of any of its arguments.

If $\mathbf{iprint}<0$, the dummy routine e04fdz can be used as lsqmon.

The specification of lsqmon is:

Fortran Interface

Subroutine lsqmon ( m, n, xc, fvec, fjac, ldfjac, s, igrade, niter, nf, iw, liw, w, lw) Integer, Intent (In) :: m, n, ldfjac, igrade, niter, nf, liw, lw Integer, Intent (Inout) :: iw(liw) Real (Kind=nag_wp), Intent (In) :: xc(n), fvec(m), fjac(ldfjac,n), s(n) Real (Kind=nag_wp), Intent (Inout) :: w(lw)

C Header Interface

#include <nagmk26.h> void  lsqmon (const Integer *m, const Integer *n, const double xc[], const double fvec[], const double fjac[], const Integer *ldfjac, const double s[], const Integer *igrade, const Integer *niter, const Integer *nf, Integer iw[], const Integer *liw, double w[], const Integer *lw)

Important: the dimension declaration for fjac must contain the variable ldfjac, not an integer constant.

1:     $\mathbf{m}$ – IntegerInput

On entry: $m$, the numbers of residuals.

2:     $\mathbf{n}$ – IntegerInput

On entry: $n$, the numbers of variables.

3:     $\mathbf{xc}\left(\mathbf{n}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: the coordinates of the current point $x$.

4:     $\mathbf{fvec}\left(\mathbf{m}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: the values of the residuals $f_i$ at the current point $x$.

5:     $\mathbf{fjac}\left(\mathbf{ldfjac},\mathbf{n}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: $\mathbf{fjac}\left(\mathit{i},\mathit{j}\right)$ contains the value of $\frac{\partial f_{\mathit{i}}}{\partial x_{\mathit{j}}}$ at the current point $x$, for $\mathit{i}=1,2,\dots ,m$ and $\mathit{j}=1,2,\dots ,n$.

6:     $\mathbf{ldfjac}$ – IntegerInput

On entry: the first dimension of the array fjac as declared in the (sub)program from which e04fcf is called.

7:     $\mathbf{s}\left(\mathbf{n}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: the singular values of the current approximation to the Jacobian matrix. Thus s may be useful as information about the structure of your problem.

8:     $\mathbf{igrade}$ – IntegerInput

On entry: e04fcf estimates the dimension of the subspace for which the Jacobian matrix can be used as a valid approximation to the curvature (see Gill and Murray (1978)). This estimate is called the grade of the Jacobian matrix, and igrade gives its current value.

9:     $\mathbf{niter}$ – IntegerInput

On entry: the number of iterations which have been performed in e04fcf.

10:   $\mathbf{nf}$ – IntegerInput

On entry: the number of times that lsqfun has been called so far. (However, for intermediate calls of lsqmon, nf is calculated on the assumption that the latest linear search has been successful. If this is not the case, the $n$ evaluations allowed for approximating the Jacobian at the new point will not in fact have been made. nf will be accurate at the final call of lsqmon.)

11:   $\mathbf{iw}\left(\mathbf{liw}\right)$ – Integer arrayWorkspace

12:   $\mathbf{liw}$ – IntegerInput

13:   $\mathbf{w}\left(\mathbf{lw}\right)$ – Real (Kind=nag_wp) arrayWorkspace

14:   $\mathbf{lw}$ – IntegerInput

These arguments correspond to the arguments iw, liw, w and lw of e04fcf. They are included in lsqmon's argument list primarily for when e04fcf is called by other Library routines.

lsqmon must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which e04fcf is called. Arguments denoted as Input must not be changed by this procedure.

Note:  you should normally print the sum of squares of residuals, so as to be able to examine the sequence of values of $F\left(x\right)$ mentioned in Section 7. It is usually helpful to print xc, the estimated gradient of the sum of squares, niter and nf.

5:     $\mathbf{iprint}$ – IntegerInput

On entry: the frequency with which lsqmon is to be called.

If $\mathbf{iprint}>0$, lsqmon is called once every iprint iterations and just before exit from e04fcf.

If $\mathbf{iprint}=0$, lsqmon is just called at the final point.

If $\mathbf{iprint}<0$, lsqmon is not called at all.

iprint should normally be set to a small positive number.

Suggested value: $\mathbf{iprint}=1$.

6:     $\mathbf{maxcal}$ – IntegerInput

On entry: the limit you set on the number of times that lsqfun may be called by e04fcf. There will be an error exit (see Section 6) after maxcal calls of lsqfun.

Suggested value: $\mathbf{maxcal}=400\times n$.

Constraint: $\mathbf{maxcal}\ge 1$.

7:     $\mathbf{eta}$ – Real (Kind=nag_wp)Input

Every iteration of e04fcf involves a linear minimization, i.e., minimization of $F\left(x^{\left(k\right)}+{\alpha }^{\left(k\right)}{p}^{\left(k\right)}\right)$ with respect to ${\alpha }^{\left(k\right)}$.

On entry: specifies how accurately the linear minimizations are to be performed. The minimum with respect to ${\alpha }^{\left(k\right)}$ will be located more accurately for small values of eta (say, $0.01$) than for large values (say, $0.9$). Although accurate linear minimizations will generally reduce the number of iterations performed by e04fcf, they will increase the number of calls of lsqfun made each iteration. On balance it is usually more efficient to perform a low accuracy minimization.

Suggested value: $\mathbf{eta}=0.5$ ($\mathbf{eta}=0.0$ if $\mathbf{n}=1$).

Constraint: $0.0\le \mathbf{eta}<1.0$.

8:     $\mathbf{xtol}$ – Real (Kind=nag_wp)Input

On entry: the accuracy in $x$ to which the solution is required.

If $x_{\mathrm{true}}$ is the true value of $x$ at the minimum, then $x_{\mathrm{sol}}$, the estimated position before a normal exit, is such that

$$‖x_{\mathrm{sol}}-x_{\mathrm{true}}‖<\mathbf{xtol}\times \left(1.0+‖x_{\mathrm{true}}‖\right)\text{,}$$

where $‖y‖=\sqrt{{\displaystyle \sum _{j=1}^n}{y}_j^2}$. For example, if the elements of $x_{\mathrm{sol}}$ are not much larger than $1.0$ in modulus and if $\mathbf{xtol}=\text{1.0E−5}$, then $x_{\mathrm{sol}}$ is usually accurate to about five decimal places. (For further details see Section 7.)

Suggested value: if $F\left(x\right)$ and the variables are scaled roughly as described in Section 9 and $\epsilon$ is the machine precision, then a setting of order $\mathbf{xtol}=\sqrt{\epsilon }$ will usually be appropriate. If xtol is set to $0.0$ or some positive value less than $10\epsilon$, e04fcf will use $10\epsilon$ instead of xtol, since $10\epsilon$ is probably the smallest reasonable setting.

Constraint: $\mathbf{xtol}\ge 0.0$.

9:     $\mathbf{stepmx}$ – Real (Kind=nag_wp)Input

On entry: an estimate of the Euclidean distance between the solution and the starting point supplied by you. (For maximum efficiency, a slight overestimate is preferable.) e04fcf will ensure that, for each iteration,

$$\sum _{j=1}^n{\left(x_j^{\left(k\right)}-x_j^{\left(k-1\right)}\right)}^2\le {\left(\mathbf{stepmx}\right)}^2\text{,}$$

where $k$ is the iteration number. Thus, if the problem has more than one solution, e04fcf is most likely to find the one nearest to the starting point. On difficult problems, a realistic choice can prevent the sequence $x^{\left(k\right)}$ entering a region where the problem is ill-behaved and can help avoid overflow in the evaluation of $F\left(x\right)$. However, an underestimate of stepmx can lead to inefficiency.

Suggested value: $\mathbf{stepmx}=100000.0$.

Constraint: $\mathbf{stepmx}\ge \mathbf{xtol}$.

10:   $\mathbf{x}\left(\mathbf{n}\right)$ – Real (Kind=nag_wp) arrayInput/Output

On entry: $\mathbf{x}\left(\mathit{j}\right)$ must be set to a guess at the $\mathit{j}$th component of the position of the minimum, for $\mathit{j}=1,2,\dots ,n$.

On exit: the final point $x^{\left(k\right)}$. Thus, if $\mathbf{ifail}=\mathbf{0}$ on exit, $\mathbf{x}\left(j\right)$ is the $j$th component of the estimated position of the minimum.

11:   $\mathbf{fsumsq}$ – Real (Kind=nag_wp)Output

On exit: the value of $F\left(x\right)$, the sum of squares of the residuals $f_i\left(x\right)$, at the final point given in x.

12:   $\mathbf{fvec}\left(\mathbf{m}\right)$ – Real (Kind=nag_wp) arrayOutput

On exit: the value of the residual $f_{\mathit{i}}\left(x\right)$ at the final point given in x, for $\mathit{i}=1,2,\dots ,m$.

13:   $\mathbf{fjac}\left(\mathbf{ldfjac},\mathbf{n}\right)$ – Real (Kind=nag_wp) arrayOutput

On exit: the estimate of the first derivative $\frac{\partial f_{\mathit{i}}}{\partial x_{\mathit{j}}}$ at the final point given in x, for $\mathit{i}=1,2,\dots ,m$ and $\mathit{j}=1,2,\dots ,n$.

14:   $\mathbf{ldfjac}$ – IntegerInput

On entry: the first dimension of the array fjac as declared in the (sub)program from which e04fcf is called.

Constraint: $\mathbf{ldfjac}\ge \mathbf{m}$.

15:   $\mathbf{s}\left(\mathbf{n}\right)$ – Real (Kind=nag_wp) arrayOutput

On exit: the singular values of the estimated Jacobian matrix at the final point. Thus s may be useful as information about the structure of your problem.

16:   $\mathbf{v}\left(\mathbf{ldv},\mathbf{n}\right)$ – Real (Kind=nag_wp) arrayOutput

On exit: the matrix $V$ associated with the singular value decomposition

$$J=US{V}^{\mathrm{T}}$$

of the estimated Jacobian matrix at the final point, stored by columns. This matrix may be useful for statistical purposes, since it is the matrix of orthonormalized eigenvectors of $J^{\mathrm{T}}J$.

17:   $\mathbf{ldv}$ – IntegerInput

On entry: the first dimension of the array v as declared in the (sub)program from which e04fcf is called.

Constraint: $\mathbf{ldv}\ge \mathbf{n}$.

18:   $\mathbf{niter}$ – IntegerOutput

On exit: the number of iterations which have been performed in e04fcf.

19:   $\mathbf{nf}$ – IntegerOutput

On exit: the number of times that the residuals have been evaluated (i.e., number of calls of lsqfun).

20:   $\mathbf{iw}\left(\mathbf{liw}\right)$ – Integer arrayWorkspace

21:   $\mathbf{liw}$ – IntegerInput

On entry: the dimension of the array iw as declared in the (sub)program from which e04fcf is called.

Constraint: $\mathbf{liw}\ge 1$.

22:   $\mathbf{w}\left(\mathbf{lw}\right)$ – Real (Kind=nag_wp) arrayWorkspace

23:   $\mathbf{lw}$ – IntegerInput

On entry: the dimension of the array w as declared in the (sub)program from which e04fcf is called.

Constraints:

• if $\mathbf{n}>1$, $\mathbf{lw}\ge 6\times \mathbf{n}+\mathbf{m}\times \mathbf{n}+2\times \mathbf{m}+\mathbf{n}\times \left(\mathbf{n}-1\right)/2$;
• if $\mathbf{n}=1$, $\mathbf{lw}\ge 7+3\times \mathbf{m}$.

24:   $\mathbf{ifail}$ – IntegerInput/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 3.4 in How to Use the NAG Library and its Documentation 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, because for this routine the values of the output arguments may be useful even if $\mathbf{ifail}\ne \mathbf{0}$ on exit, the recommended value is $-1$. When the value $-\mathbf{1}\text{ or }1$ 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

$\mathbf{ifail}<0$

A negative value of ifail indicates an exit from e04fcf because you have set iflag negative in lsqfun. The value of ifail will be the same as your setting of iflag.

$\mathbf{ifail}=1$

On entry,	$\mathbf{n}<1$,
or	$\mathbf{m}<\mathbf{n}$,
or	$\mathbf{maxcal}<1$,
or	$\mathbf{eta}<0.0$,
or	$\mathbf{eta}\ge 1.0$,
or	$\mathbf{xtol}<0.0$,
or	$\mathbf{stepmx}<\mathbf{xtol}$,
or	$\mathbf{ldfjac}<\mathbf{m}$,
or	$\mathbf{ldv}<\mathbf{n}$,
or	$\mathbf{liw}<1$,
or	$\mathbf{lw}<6\times \mathbf{n}+\mathbf{m}\times \mathbf{n}+2\times \mathbf{m}+\mathbf{n}\times \left(\mathbf{n}-1\right)/2$, when $\mathbf{n}>1$,
or	$\mathbf{lw}<7+3\times \mathbf{m}$, when $\mathbf{n}=1$.

When this exit occurs, no values will have been assigned to fsumsq, or to the elements of fvec, fjac, s or v.

$\mathbf{ifail}=2$

There have been maxcal calls of lsqfun. If steady reductions in the sum of squares, $F\left(x\right)$, were monitored up to the point where this exit occurred, then the exit probably occurred simply because maxcal was set too small, so the calculations should be restarted from the final point held in x. This exit may also indicate that $F\left(x\right)$ has no minimum.

$\mathbf{ifail}=3$

The conditions for a minimum have not all been satisfied, but a lower point could not be found. This could be because xtol has been set so small that rounding errors in the evaluation of the residuals make attainment of the convergence conditions impossible.

$\mathbf{ifail}=4$

The method for computing the singular value decomposition of the estimated Jacobian matrix has failed to converge in a reasonable number of sub-iterations. It may be worth applying e04fcf again starting with an initial approximation which is not too close to the point at which the failure occurred.

$\mathbf{ifail}=-99$

An unexpected error has been triggered by this routine. Please contact NAG.

See Section 3.9 in How to Use the NAG Library and its Documentation for further information.

$\mathbf{ifail}=-399$

Your licence key may have expired or may not have been installed correctly.

See Section 3.8 in How to Use the NAG Library and its Documentation for further information.

$\mathbf{ifail}=-999$

Dynamic memory allocation failed.

See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

7

Accuracy

8

Parallelism and Performance

9

Further Comments

10

Example

10.1

Program Text

Program Text (e04fcfe.f90)

10.2

Program Data

Program Data (e04fcfe.d)

10.3

Program Results

Program Results (e04fcfe.r)