NAG FL Interface
e04gbf (lsq_​uncon_​quasi_​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: $\mathbf{lsqlin}$ – Subroutine, supplied by the NAG Library. External Procedure

lsqlin enables you to specify whether the linear minimizations (i.e., minimizations of $F(x^{\left(k\right)}+{\alpha }^{\left(k\right)}{p}^{\left(k\right)})$ with respect to ${\alpha }^{\left(k\right)}$) are to be performed by a routine which just requires the evaluation of the $f_i\left(x\right)$ (e04fcv), or by a routine which also requires the first derivatives of the $f_i\left(x\right)$ (e04hev).

It will often be possible to evaluate the first derivatives of the residuals in about the same amount of computer time that is required for the evaluation of the residuals themselves – if this is so, then e04gbf should be called with routine e04hev as the argument lsqlin. However, if the evaluation of the derivatives takes more than about four times as long as the evaluation of the residuals, then e04fcv will usually be preferable. If in doubt, use e04hev as it is slightly more robust.

Whichever subroutine is used, must be declared as EXTERNAL in the subroutine from which e04gbf is called.

4: $\mathbf{lsqfun}$ – 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 e04gbf to terminate immediately.)

The specification of lsqfun is:

Fortran Interface copy

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 copy

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 copy

#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) }

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

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

On entry: will be set to $0$, $1$ or $2$.

$\mathbf{iflag}=0$

Indicates that only the residuals need to be evaluated

$\mathbf{iflag}=1$

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.

If e04hev is used as e04gbf's lsqlin, lsqfun will always be called with iflag set to $2$.

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 is wished to stop the calculations for any other reason), you should reset iflag to some negative number and return control to e04gbf. e04gbf will then terminate immediately, with ifail set to your setting of iflag.

2: $\mathbf{m}$ – Integer Input

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

3: $\mathbf{n}$ – Integer Input

On entry: $n$, the number 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: $\mathbf{fjac}(\mathbf{ldfjac},\mathbf{n})$ – Real (Kind=nag_wp) array Output

On exit: unless $\mathbf{iflag}=0$ on entry, or iflag is reset to a negative number, $\mathbf{fjac}(\mathit{i},\mathit{j})$ 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 e04gbf.

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 e04gbf'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 e04gbf by using partitions of iw and w beyond those used as workspace by e04gbf. 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 e04gbf.

lsqfun must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which e04gbf 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 e04gbf. If your code inadvertently does return any NaNs or infinities, e04gbf is likely to produce unexpected results.

lsqfun should be tested separately before being used in conjunction with e04gbf.

5: $\mathbf{lsqmon}$ – 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 NAG Library dummy routine e04fdz can be used as lsqmon.

The specification of lsqmon is:

Fortran Interface copy

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 copy

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 copy

#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) }

Important: 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: $\mathbf{fjac}(\mathbf{ldfjac},\mathbf{n})$ – Real (Kind=nag_wp) array Input

On entry: $\mathbf{fjac}(\mathit{i},\mathit{j})$ 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 e04gbf 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.

8: $\mathbf{igrade}$ – Integer Input

On entry: e04gbf 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 e04gbf.

10: $\mathbf{nf}$ – Integer Input

On entry: the number of evaluations of the residuals. (If e04hev is used as lsqlin, nf is also the number of evaluations of the Jacobian matrix.)

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 e04gbf. They are included in lsqmon's argument list primarily for when e04gbf 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 e04gbf 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 also print xc, the gradient of the sum of squares, niter and nf.

6: $\mathbf{iprint}$ – 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 e04gbf.

$\mathbf{iprint}=0$

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: $\mathbf{iprint}=1$.

7: $\mathbf{maxcal}$ – Integer Input

On entry: enables you to limit the number of times that lsqfun is called by e04gbf. There will be an error exit (see Section 6) after maxcal calls of lsqfun.

Suggested values:

• $\mathbf{maxcal}=75\times n$ if e04fcv is used as lsqlin,
• $\mathbf{maxcal}=50\times n$ if e04hev is used as lsqlin.

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

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

On entry: every iteration of e04gbf involves a linear minimization (i.e., minimization of $F(x^{\left(k\right)}+{\alpha }^{\left(k\right)}{p}^{\left(k\right)})$ 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 performed by e04gbf, they will increase the number of calls of lsqfun made every iteration. On balance it is usually more efficient to perform a low accuracy minimization.

Suggested values:

• $\mathbf{eta}=0.9$ if $\mathbf{n}>1$ and e04hev is used as lsqlin,
• $\mathbf{eta}=0.5$ if $\mathbf{n}>1$ and e04fcv is uses as lsqlin,
• $\mathbf{eta}=0.0$ if $\mathbf{n}=1$.

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

9: $\mathbf{xtol}$ – 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

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

where $\Vert y\Vert =\sqrt{{\displaystyle \sum _{j=1}^n}{y}_j^2}$. 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 $10\epsilon$, e04gbf will use $10\epsilon$ instead of xtol, since $10\epsilon$ is probably the smallest reasonable setting.

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

10: $\mathbf{stepmx}$ – 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.)

e04gbf will ensure that, for each iteration,

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

where $k$ is the iteration number. Thus, if the problem has more than one solution, e04gbf 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: $\mathbf{stepmx}\ge \mathbf{xtol}$.

11: $\mathbf{x}\left(\mathbf{n}\right)$ – 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.

12: $\mathbf{fsumsq}$ – 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.

13: $\mathbf{fvec}\left(\mathbf{m}\right)$ – 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 $\mathit{i}=1,2,\dots ,m$.

14: $\mathbf{fjac}(\mathbf{ldfjac},\mathbf{n})$ – 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 $\mathit{j}=1,2,\dots ,n$.

15: $\mathbf{ldfjac}$ – Integer Input

On entry: the first dimension of the array fjac as declared in the (sub)program from which e04gbf is called.

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

16: $\mathbf{s}\left(\mathbf{n}\right)$ – 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.

17: $\mathbf{v}(\mathbf{ldv},\mathbf{n})$ – 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 $J^{\mathrm{T}}J$.

18: $\mathbf{ldv}$ – Integer Input

On entry: the first dimension of the array v as declared in the (sub)program from which e04gbf is called.

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

19: $\mathbf{niter}$ – Integer Output

On exit: the number of iterations which have been performed in e04gbf.

20: $\mathbf{nf}$ – Integer Output

On exit: the number of times that the residuals have been evaluated (i.e., the number of calls of lsqfun). If e04hev is used as lsqlin, nf is also the number of times that the Jacobian matrix has been evaluated.

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

22: $\mathbf{liw}$ – Integer Input

On entry: the dimension of the array iw as declared in the (sub)program from which e04gbf is called.

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

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

24: $\mathbf{lw}$ – Integer Input

On entry: the dimension of the array w as declared in the (sub)program from which e04gbf 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 $\mathbf{n}=1$, $\mathbf{lw}\ge 9+3\times \mathbf{m}$.

25: $\mathbf{ifail}$ – 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 $\mathbf{ifail}\ne \mathbf{0}$ on exit. When the value $-\mathbf{1}$ or $\mathbf{1}$ 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 $\mathbf{maxcal}=⟨\mathit{\text{value}}⟩$ calls to 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.

The error may also be caused by mistakes in lsqfun, by the formulation of the problem or by an awkward function. If there are no such mistakes, it is worth restarting the calculations from a different starting point (not the point at which the failure occurred) in order to avoid the region which caused the failure.

$\mathbf{ifail}=3$

The conditions for a minimum have not all been satisfied, but a lower point could not be found. See Section 7 for further information.

The error may also be caused by mistakes in lsqfun, by the formulation of the problem or by an awkward function. If there are no such mistakes, it is worth restarting the calculations from a different starting point (not the point at which the failure occurred) in order to avoid the region which caused the failure.

$\mathbf{ifail}=4$

Failure in computing SVD of Jacobian matrix.

It may be worth applying e04gbf again starting with an initial approximation which is not too close to the point at which the failure occurred.

The error may also be caused by mistakes in lsqfun, by the formulation of the problem or by an awkward function. If there are no such mistakes, it is worth restarting the calculations from a different starting point (not the point at which the failure occurred) in order to avoid the region which caused the failure.

$\mathbf{ifail}<0$

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