NAG FL Interface
e04kzf (bounds_​mod_​deriv_​easy)

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

1: $\mathbf{n}$ – Integer Input

On entry: the number $n$ of independent variables.

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

2: $\mathbf{ibound}$ – Integer Input

On entry: indicates whether the facility for dealing with bounds of special forms is to be used. It must be set to one of the following values:

$\mathbf{ibound}=0$

If you are supplying all the $l_j$ and $u_j$ individually.

$\mathbf{ibound}=1$

If there are no bounds on any $x_j$.

$\mathbf{ibound}=2$

If all the bounds are of the form $0\le x_j$.

$\mathbf{ibound}=3$

If $l_1=l_2=\cdots =l_n$ and $u_1=u_2=\cdots =u_n$.

Constraint: $0\le \mathbf{ibound}\le 3$.

3: $\mathbf{funct2}$ – Subroutine, supplied by the user. External Procedure

You must supply this routine to calculate the values of the function $F\left(x\right)$ and its first derivatives $\frac{\partial F}{\partial x_j}$ at any point $x$. It should be tested separately before being used in conjunction with e04kzf (see Chapter E04).

The specification of funct2 is:

Fortran Interface copy

Subroutine funct2 ( n, xc, fc, gc, iuser, ruser) Integer, Intent (In) :: n Integer, Intent (Inout) :: iuser(*) Real (Kind=nag_wp), Intent (In) :: xc(n) Real (Kind=nag_wp), Intent (Inout) :: ruser(*) Real (Kind=nag_wp), Intent (Out) :: fc, gc(n)

C Header Interface copy

void  funct2 (const Integer *n, const double xc[], double *fc, double gc[], Integer iuser[], double ruser[])

C++ Header Interface copy

#include <nag.h> extern "C" { void  funct2 (const Integer &n, const double xc[], double &fc, double gc[], Integer iuser[], double ruser[]) }

1: $\mathbf{n}$ – Integer Input

On entry: the number $n$ of variables.

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

On entry: the point $x$ at which the function and derivatives are required.

3: $\mathbf{fc}$ – Real (Kind=nag_wp) Output

On exit: the value of the function $F$ at the current point $x$,

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

On exit: $\mathbf{gc}\left(\mathit{j}\right)$ must be set to the value of the first derivative $\frac{\partial F}{\partial x_{\mathit{j}}}$ at the point $x$, for $\mathit{j}=1,2,\dots ,n$.

5: $\mathbf{iuser}\left(*\right)$ – Integer array User Workspace

6: $\mathbf{ruser}\left(*\right)$ – Real (Kind=nag_wp) array User Workspace

funct2 is called with the arguments iuser and ruser as supplied to e04kzf. You should use the arrays iuser and ruser to supply information to funct2.

funct2 must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which e04kzf is called. Arguments denoted as Input must not be changed by this procedure.

Note: funct2 should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by e04kzf. If your code inadvertently does return any NaNs or infinities, e04kzf is likely to produce unexpected results.

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

On entry: the lower bounds $l_j$.

If ibound is set to $0$, you must set $\mathbf{bl}\left(\mathit{j}\right)$ to $l_{\mathit{j}}$, for $\mathit{j}=1,2,\dots ,n$. (If a lower bound is not specified for a particular $x_{\mathit{j}}$, the corresponding $\mathbf{bl}\left(\mathit{j}\right)$ should be set to $-{10}^6$.)

If ibound is set to $3$, you must set $\mathbf{bl}\left(1\right)$ to $l_1$; e04kzf will then set the remaining elements of bl equal to $\mathbf{bl}\left(1\right)$.

On exit: the lower bounds actually used by e04kzf.

5: $\mathbf{bu}\left(\mathbf{n}\right)$ – Real (Kind=nag_wp) array Input/Output

On entry: the upper bounds $u_j$.

If ibound is set to $0$, you must set $\mathbf{bu}\left(\mathit{j}\right)$ to $u_{\mathit{j}}$, for $\mathit{j}=1,2,\dots ,n$. (If an upper bound is not specified for a particular $x_j$, the corresponding $\mathbf{bu}\left(j\right)$ should be set to ${10}^6$.)

If ibound is set to $3$, you must set $\mathbf{bu}\left(1\right)$ to $u_1$; e04kzf will then set the remaining elements of bu equal to $\mathbf{bu}\left(1\right)$.

On exit: the upper bounds actually used by e04kzf.

6: $\mathbf{x}\left(\mathbf{n}\right)$ – 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 $\mathit{j}=1,2,\dots ,n$. The routine checks the gradient at the starting point, and is more likely to detect any error in your programming if the initial $\mathbf{x}\left(j\right)$ are nonzero and mutually distinct.

On exit: the lowest point found during the calculations of the position of the minimum.

7: $\mathbf{f}$ – Real (Kind=nag_wp) Output

On exit: the value of $F\left(x\right)$ corresponding to the final point stored in x.

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

On exit: the value of $\frac{\partial F}{\partial x_{\mathit{j}}}$ corresponding to the final point stored in x, for $\mathit{j}=1,2,\dots ,n$; the value of $\mathbf{g}\left(j\right)$ for variables not on a bound should normally be close to zero.

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

10: $\mathbf{liw}$ – Integer Input

On entry: the dimension of the array iw as declared in the (sub)program from which e04kzf is called.

Constraint: $\mathbf{liw}\ge \mathbf{n}+2$.

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

12: $\mathbf{lw}$ – Integer Input

On entry: the dimension of the array w as declared in the (sub)program from which e04kzf is called.

Constraint: $\mathbf{lw}\ge \max (\mathbf{n}\times (\mathbf{n}+7),10)$.

13: $\mathbf{iuser}\left(*\right)$ – Integer array User Workspace

14: $\mathbf{ruser}\left(*\right)$ – Real (Kind=nag_wp) array User Workspace

iuser and ruser are not used by e04kzf, but are passed directly to funct2 and may be used to pass information to this routine.

15: $\mathbf{ifail}$ – 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, $\mathbf{ibound}=⟨\mathit{\text{value}}⟩$.
Constraint: $0\le \mathbf{ibound}\le 3$.

On entry, $\mathbf{ibound}=0$ and $\mathbf{bl}\left(\mathit{j}\right)>\mathbf{bu}\left(\mathit{j}\right)$ for some $j$.

On entry, $\mathbf{ibound}=3$ and $\mathbf{bl}\left(1\right)>\mathbf{bu}\left(1\right)$.

On entry, $\mathbf{liw}=⟨\mathit{\text{value}}⟩$.
Constraint: $\mathbf{liw}\ge ⟨\mathit{\text{value}}⟩$.

On entry, $\mathbf{lw}=⟨\mathit{\text{value}}⟩$.
Constraint: $\mathbf{lw}\ge ⟨\mathit{\text{value}}⟩$.

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

$\mathbf{ifail}=2$

There have been $50\times \mathbf{n}$ function evaluations.

The algorithm does not seem to be converging. The calculations can be restarted from the final point held in x. The error 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.

$\mathbf{ifail}=5$

It is probable that a local minimum has been found, but it cannot be guaranteed.

$\mathbf{ifail}=6$

It is possible that a local minimum has been found, but it cannot be guaranteed.

$\mathbf{ifail}=7$

It is unlikely that a local minimum has been found.

$\mathbf{ifail}=8$

It is very unlikely that a local minimum has been found.

$\mathbf{ifail}=9$

The modulus of a variable has become very large. There may be a mistake in funct2, your problem has no finite solution, or the problem needs rescaling.

$\mathbf{ifail}=10$

It is very likely that you have made an error forming the gradient.

$\mathbf{ifail}=-99$

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