NAG Library Routine Document
E04KZF
1 Purpose
E04KZF is an easytouse modified Newton algorithm for finding a minimum of a function $F\left({x}_{1},{x}_{2},\dots ,{x}_{n}\right)$, subject to fixed upper and lower bounds on the independent variables ${x}_{1},{x}_{2},\dots ,{x}_{n}$, when first derivatives of $F$ are available. It is intended for functions which are continuous and which have continuous first and second derivatives (although it will usually work even if the derivatives have occasional discontinuities).
2 Specification
SUBROUTINE E04KZF ( 
N, IBOUND, FUNCT2, BL, BU, X, F, G, IW, LIW, W, LW, IUSER, RUSER, IFAIL) 
INTEGER 
N, IBOUND, IW(LIW), LIW, LW, IUSER(*), IFAIL 
REAL (KIND=nag_wp) 
BL(N), BU(N), X(N), F, G(N), W(LW), RUSER(*) 
EXTERNAL 
FUNCT2 

3 Description
E04KZF is applicable to problems of the form:
when first derivatives are known.
Special provision is made for problems which actually have no bounds on the ${x}_{j}$, problems which have only nonnegativity bounds, and problems in which ${l}_{1}={l}_{2}=\cdots ={l}_{n}$ and ${u}_{1}={u}_{2}=\cdots ={u}_{n}$. You must supply a subroutine to calculate the values of $F\left(x\right)$ and its first derivatives at any point $x$.
From a starting point you supplied there is generated, on the basis of estimates of the gradient of the curvature of $F\left(x\right)$, a sequence of feasible points which is intended to converge to a local minimum of the constrained function.
4 References
Gill P E and Murray W (1976) Minimization subject to bounds on the variables NPL Report NAC 72 National Physical Laboratory
5 Arguments
 1: $\mathrm{N}$ – INTEGERInput

On entry: the number $n$ of independent variables.
Constraint:
${\mathbf{N}}\ge 1$.
 2: $\mathrm{IBOUND}$ – INTEGERInput

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: $\mathrm{FUNCT2}$ – 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:
INTEGER 
N, IUSER(*) 
REAL (KIND=nag_wp) 
XC(N), FC, GC(N), RUSER(*) 

 1: $\mathrm{N}$ – INTEGERInput

On entry: the number $n$ of variables.
 2: $\mathrm{XC}\left({\mathbf{N}}\right)$ – REAL (KIND=nag_wp) arrayInput

On entry: the point $x$ at which the function and derivatives are required.
 3: $\mathrm{FC}$ – REAL (KIND=nag_wp)Output

On exit: the value of the function $F$ at the current point $x$,
 4: $\mathrm{GC}\left({\mathbf{N}}\right)$ – REAL (KIND=nag_wp) arrayOutput

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: $\mathrm{IUSER}\left(*\right)$ – INTEGER arrayUser Workspace
 6: $\mathrm{RUSER}\left(*\right)$ – REAL (KIND=nag_wp) arrayUser 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.
 4: $\mathrm{BL}\left({\mathbf{N}}\right)$ – REAL (KIND=nag_wp) arrayInput/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: $\mathrm{BU}\left({\mathbf{N}}\right)$ – REAL (KIND=nag_wp) arrayInput/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: $\mathrm{X}\left({\mathbf{N}}\right)$ – 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 $\mathit{j}=1,2,\dots ,n$. 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: $\mathrm{F}$ – REAL (KIND=nag_wp)Output

On exit: the value of
$F\left(x\right)$ corresponding to the final point stored in
X.
 8: $\mathrm{G}\left({\mathbf{N}}\right)$ – REAL (KIND=nag_wp) arrayOutput

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: $\mathrm{IW}\left({\mathbf{LIW}}\right)$ – INTEGER arrayWorkspace
 10: $\mathrm{LIW}$ – INTEGERInput

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: $\mathrm{W}\left({\mathbf{LW}}\right)$ – REAL (KIND=nag_wp) arrayWorkspace
 12: $\mathrm{LW}$ – INTEGERInput

On entry: the dimension of the array
W as declared in the (sub)program from which E04KZF is called.
Constraint:
${\mathbf{LW}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{N}}\times \left({\mathbf{N}}+7\right),10\right)$.
 13: $\mathrm{IUSER}\left(*\right)$ – INTEGER arrayUser Workspace
 14: $\mathrm{RUSER}\left(*\right)$ – REAL (KIND=nag_wp) arrayUser Workspace

IUSER and
RUSER are not used by E04KZF, but are passed directly to
FUNCT2 and should be used to pass information to this routine.
 15: $\mathrm{IFAIL}$ – 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
If on entry
${\mathbf{IFAIL}}=0$ or
$1$, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
Note: E04KZF may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the routine:
 ${\mathbf{IFAIL}}=1$

On entry,  ${\mathbf{N}}<1$, 
or  ${\mathbf{IBOUND}}<0$, 
or  ${\mathbf{IBOUND}}>3$, 
or  ${\mathbf{IBOUND}}=0$ and ${\mathbf{BL}}\left(j\right)>{\mathbf{BU}}\left(j\right)$ for some $j$, 
or  ${\mathbf{IBOUND}}=3$ and ${\mathbf{BL}}\left(1\right)>{\mathbf{BU}}\left(1\right)$, 
or  ${\mathbf{LIW}}<{\mathbf{N}}+2$, 
or  ${\mathbf{LW}}<\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(10,{\mathbf{N}}\times \left({\mathbf{N}}+7\right)\right)$. 
 ${\mathbf{IFAIL}}=2$

There has been a large number of function evaluations, yet 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 met but a lower point could not be found and the algorithm has failed.
 ${\mathbf{IFAIL}}=4$

Not used. (This value of the argument is included to make the significance of ${\mathbf{IFAIL}}={\mathbf{5}}$ etc. consistent in the easytouse routines.)
 ${\mathbf{IFAIL}}=5$
 ${\mathbf{IFAIL}}=6$
 ${\mathbf{IFAIL}}=7$
 ${\mathbf{IFAIL}}=8$

There is some doubt about whether the point
$x$ found by E04KZF is a minimum. The degree of confidence in the result decreases as
IFAIL increases. Thus, when
${\mathbf{IFAIL}}={\mathbf{5}}$ it is probable that the final
$x$ gives a good estimate of the position of a minimum, but when
${\mathbf{IFAIL}}={\mathbf{8}}$ it is very unlikely that the routine has found a minimum.
 ${\mathbf{IFAIL}}=9$

In the search for a minimum, the modulus of one of the variables has become very large
$\left(\sim {10}^{6}\right)$. This indicates that there is a mistake in
FUNCT2, that your problem has no finite solution, or that the problem needs rescaling (see
Section 9).
 ${\mathbf{IFAIL}}=10$
It is very likely that you have made an error in forming the gradient.
 ${\mathbf{IFAIL}}=99$
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.
If you are dissatisfied with the result (e.g., because
${\mathbf{IFAIL}}={\mathbf{5}}$,
${\mathbf{6}}$,
${\mathbf{7}}$ or
${\mathbf{8}}$), 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. If persistent trouble occurs and it is possible to calculate second derivatives it may be advisable to change to a routine which uses second derivatives (see the
E04 Chapter Introduction).
7 Accuracy
When a successful exit is made then, for a computer with a mantissa of $t$ decimals, one would expect to get about $t/21$ decimals accuracy in $x$ and about $t1$ decimals accuracy in $F$, provided the problem is reasonably well scaled.
8 Parallelism and Performance
E04KZF is not threaded in any implementation.
The number of iterations required depends on the number of variables, the behaviour of
$F\left(x\right)$ and the distance of the starting point from the solution. The number of operations performed in an iteration of E04KZF is roughly proportional to
${n}^{3}+\mathit{O}\left({n}^{2}\right)$. In addition, each iteration makes at least
$m+1$ calls of
FUNCT2 where
$m$ is the number of variables not fixed on bounds. So unless
$F\left(x\right)$ and the gradient vector can be evaluated very quickly, the run time will be dominated by the time spent in
FUNCT2.
Ideally the problem should be scaled so that at the solution the value of $F\left(x\right)$ and the corresponding values of ${x}_{1},{x}_{2},\dots ,{x}_{n}$ are in the range $\left(1,+1\right)$, and so that at points a unit distance away from the solution, $F$ is approximately a unit value greater than at the minimum. It is unlikely that you will be able to follow these recommendations very closely, but it is worth trying (by guesswork), as sensible scaling will reduce the difficulty of the minimization problem, so that E04KZF will take less computer time.
10 Example
A program to minimize
subject to
starting from the initial guess
$\left(3,1,0,1\right)$.
In practice, it is worth trying to make
FUNCT2 as efficient as possible. This has not been done in the example program for reasons of clarity.
10.1 Program Text
Program Text (e04kzfe.f90)
10.2 Program Data
None.
10.3 Program Results
Program Results (e04kzfe.r)