NAG FL Interface
e04gdf (lsq_​uncon_​mod_​deriv_​comp)

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

1: $\mathbf{m}$ – Integer Input

2: $\mathbf{n}$ – Integer Input

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 $f_i\left(x\right)$ and Jacobian matrix of first derivatives $\frac{\partial f_i}{\partial x_j}$ at any point $x$. (However, if you do not wish to calculate the residuals or first derivatives at a particular $x$, there is the option of setting an argument to cause e04gdf to terminate immediately.)

The specification of lsqfun is:

Fortran Interface

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

C Header Interface

void  lsqfun_ (Integer *iflag, const Integer *m, const Integer *n, const double xc[], double fvec[], double fjac[], const Integer *ldfjac, Integer iw[], const Integer *liw, double w[], const Integer *lw)

C++ Header Interface

#include <nag.h> extern "C" { void  lsqfun_ (Integer &iflag, const Integer &m, const Integer &n, const double xc[], double fvec[], double fjac[], const Integer &ldfjac, Integer iw[], const Integer &liw, double w[], const Integer &lw) }

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

1: $\mathbf{iflag}$ – Integer Input/Output

On entry: to lsqfun, iflag will be set to $1$ or $2$.

$\mathbf{iflag}=1$

Indicates that only the Jacobian matrix needs to be evaluated

$\mathbf{iflag}=2$

Indicates that both the residuals and the Jacobian matrix must be calculated

On exit: if it is not possible to evaluate the $f_i\left(x\right)$ or their first derivatives at the point given in xc (or if it wished to stop the calculations for any other reason), you should reset iflag to some negative number and return control to e04gdf. e04gdf will then terminate immediately, with ifail set to your setting of iflag.

2: $\mathbf{m}$ – Integer Input

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

3: $\mathbf{n}$ – Integer Input

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

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

On entry: the point $x$ at which the values of the $f_i$ and the $\frac{\partial f_i}{\partial x_j}$ are required.

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

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

6: $\mathbf{fjac}\left(\mathbf{ldfjac},\mathbf{n}\right)$ – Real (Kind=nag_wp) array Output

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

7: $\mathbf{ldfjac}$ – Integer Input

On entry: the first dimension of the array fjac, set to $m$ by e04gdf.

8: $\mathbf{iw}\left(\mathbf{liw}\right)$ – Integer array Workspace

9: $\mathbf{liw}$ – Integer Input

10: $\mathbf{w}\left(\mathbf{lw}\right)$ – Real (Kind=nag_wp) array Workspace

11: $\mathbf{lw}$ – Integer Input

lsqfun is called with e04gdf's arguments iw, liw, w, lw as these arguments. They are present so that, when other library routines require the solution of a minimization subproblem, constants needed for the evaluation of residuals can be passed through iw and w. Similarly, you could pass quantities to lsqfun from the segment which calls e04gdf by using partitions of iw and w beyond those used as workspace by e04gdf. However, because of the danger of mistakes in partitioning, it is recommended that you should pass information to lsqfun via COMMON global variables and not use iw or w at all. In any case you must not change the elements of iw and w used as workspace by e04gdf.

lsqfun must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which e04gdf 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 e04gdf. If your code inadvertently does return any NaNs or infinities, e04gdf is likely to produce unexpected results.

lsqfun should be tested separately before being used in conjunction with e04gdf.

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

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)

C++ Header Interface

#include <nag.h> extern "C" { 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) }

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

1: $\mathbf{m}$ – Integer Input

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

2: $\mathbf{n}$ – Integer Input

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

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

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

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

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) array Input

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: $\mathbf{ldfjac}$ – Integer Input

On entry: the first dimension of the array fjac as declared in the (sub)program from which e04gdf is called.

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

On entry: the singular values of the current Jacobian matrix. Thus s may be useful as information about the structure of your problem. (If $\mathbf{iprint}>0$, lsqmon is called at the initial point before the singular values have been calculated. So the elements of s are set to zero for the first call of lsqmon.)

8: $\mathbf{igrade}$ – Integer Input

On entry: e04gdf 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: $\mathbf{niter}$ – Integer Input

On entry: the number of iterations which have been performed in e04gdf.

10: $\mathbf{nf}$ – Integer Input

On entry: the number of times that lsqfun has been called so far with $\mathbf{iflag}=2$. (In addition to these calls monitored by nf, lsqfun is called not more than n times per iteration with iflag set to $1$.)

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

12: $\mathbf{liw}$ – Integer Input

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

14: $\mathbf{lw}$ – Integer Input

As in lsqfun, these arguments correspond to the arguments iw, liw, w, lw of e04gdf. They are included in lsqmon's argument list primarily for when e04gdf 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 e04gdf 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 also helpful to print xc, the gradient of the sum of squares, niter and nf.

5: $\mathbf{iprint}$ – Integer Input

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

$\mathbf{iprint}>0$

lsqmon is called once every iprint iterations and just before exit from e04gdf.

$\mathbf{iprint}=0$

lsqmon is just called at the final point.

$\mathbf{iprint}<0$

lsqmon is not called at all.

iprint should normally be set to a small positive number.

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

6: $\mathbf{maxcal}$ – Integer Input

On entry: enables you to limit the number of times that lsqfun is called by e04gdf. There will be an error exit (see Section 6) after maxcal evaluations of the residuals (i.e., calls of lsqfun with iflag set to $2$). It should be borne in mind that, in addition to the calls of lsqfun which are limited directly by maxcal, there will be calls of lsqfun (with iflag set to $1$) to evaluate only first derivatives.

Suggested value: $\mathbf{maxcal}=50\times n$.

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

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

On entry: every iteration of e04gdf 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 ${\alpha }^{\left(k\right)}$. eta specifies how accurately these 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, they will tend to increase the number of calls of lsqfun (with iflag set to $2$) needed for each linear minimization. On balance it is usually efficient to perform a low accuracy linear 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

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

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.)

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 $10\epsilon$, e04gdf 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.) e04gdf will ensure that, for each iteration,

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

where $k$ is the iteration number. Thus, if the problem has more than one solution, e04gdf is most likely to find the one nearest to the starting point. On difficult problems, a realistic choice can prevent the sequence of $x^{\left(k\right)}$ 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) array Input/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) array Output

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) array Output

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

14: $\mathbf{ldfjac}$ – Integer Input

On entry: the first dimension of the array fjac as declared in the (sub)program from which e04gdf is called.

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

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

On exit: the singular values of the 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) array Output

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

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

of the 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: $\mathbf{ldv}$ – Integer Input

On entry: the first dimension of the array v as declared in the (sub)program from which e04gdf is called.

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

18: $\mathbf{niter}$ – Integer Output

On exit: the number of iterations which have been performed in e04gdf.

19: $\mathbf{nf}$ – Integer Output

On exit: the number of times that the residuals have been evaluated (i.e., number of calls of lsqfun with iflag set to $2$).

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

21: $\mathbf{liw}$ – Integer Input

On entry: the dimension of the array iw as declared in the (sub)program from which e04gdf is called.

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

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

23: $\mathbf{lw}$ – Integer Input

On entry: the dimension of the array w as declared in the (sub)program from which e04gdf is called.

Constraints:

• if $\mathbf{n}>1$, $\mathbf{lw}\ge 7\times \mathbf{n}+\mathbf{m}\times \mathbf{n}+2\times \mathbf{m}+\mathbf{n}\times \mathbf{n}$;
• if $\mathbf{n}=1$, $\mathbf{lw}\ge 9+3\times \mathbf{m}$.

24: $\mathbf{ifail}$ – 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, 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

$\mathbf{ifail}=1$

On entry, $\mathbf{eta}=〈\mathit{\text{value}}〉$.
Constraint: $0.0\le \mathbf{eta}<1.0$.

On entry, $\mathbf{ldfjac}=〈\mathit{\text{value}}〉$ and $\mathbf{m}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{ldfjac}\ge \mathbf{m}$.

On entry, $\mathbf{ldv}=〈\mathit{\text{value}}〉$ and $\mathbf{n}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{ldv}\ge \mathbf{n}$.

On entry, $\mathbf{liw}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{liw}\ge 1$.

On entry, $\mathbf{m}=〈\mathit{\text{value}}〉$ and $\mathbf{n}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{m}\ge \mathbf{n}$.

On entry, $\mathbf{maxcal}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{maxcal}\ge 1$.

On entry, $\mathbf{n}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{n}\ge 1$.

On entry, $\mathbf{n}=1$ and $\mathbf{lw}=〈\mathit{\text{value}}〉$.
Constraint: if $\mathbf{n}=1$ then $\mathbf{lw}\ge 9+3\times \mathbf{m}$; that is, $〈\mathit{\text{value}}〉$.

On entry, $\mathbf{n}>1$ and $\mathbf{lw}=〈\mathit{\text{value}}〉$.
Constraint: if $\mathbf{n}>1$ then $\mathbf{lw}\ge 7\times \mathbf{n}+\mathbf{m}\times \mathbf{n}+2\times \mathbf{m}+{\mathbf{n}}^2$; that is, $〈\mathit{\text{value}}〉$.

On entry, $\mathbf{stepmx}=〈\mathit{\text{value}}〉$ and $\mathbf{xtol}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{stepmx}\ge \mathbf{xtol}$.

On entry, $\mathbf{xtol}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{xtol}\ge 0.0$.

$\mathbf{ifail}=2$

There have been $\mathbf{maxcal}=〈\mathit{\text{value}}〉$ evaluations of the residuals.

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. See Section 7 for further information.

$\mathbf{ifail}=4$

Failure in computing SVD of Jacobian matrix.

It may be worth applying e04gdf again starting with an initial approximation which is not too close to the point at which the failure occurred.

$\mathbf{ifail}<0$

User requested termination by setting iflag negative in lsqfun.

$\mathbf{ifail}=-99$

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.

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results