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Chapter Contents
Chapter Introduction
NAG Toolbox

# NAG Toolbox: nag_opt_bounds_mod_deriv_easy (e04kz)

## Purpose

nag_opt_bounds_mod_deriv_easy (e04kz) is an easy-to-use 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).

## Syntax

[bl, bu, x, f, g, user, ifail] = e04kz(ibound, funct2, bl, bu, x, 'n', n, 'user', user)
[bl, bu, x, f, g, user, ifail] = nag_opt_bounds_mod_deriv_easy(ibound, funct2, bl, bu, x, 'n', n, 'user', user)

## Description

nag_opt_bounds_mod_deriv_easy (e04kz) is applicable to problems of the form:
 $Minimize⁡Fx1,x2,…,xn subject to lj≤xj≤uj, j=1,2,…,n$
when first derivatives are known.
Special provision is made for problems which actually have no bounds on the ${x}_{j}$, problems which have only non-negativity bounds, and problems in which ${l}_{1}={l}_{2}=\cdots ={l}_{n}$ and ${u}_{1}={u}_{2}=\cdots ={u}_{n}$. You must supply a function 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.

## References

Gill P E and Murray W (1976) Minimization subject to bounds on the variables NPL Report NAC 72 National Physical Laboratory

## Parameters

### Compulsory Input Parameters

1:     $\mathrm{ibound}$int64int32nag_int scalar
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: $0\le {\mathbf{ibound}}\le 3$.
2:     $\mathrm{funct2}$ – function handle or string containing name of m-file
You must supply this function 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 nag_opt_bounds_mod_deriv_easy (e04kz) (see Chapter E04).
[fc, gc, user] = funct2(n, xc, user)

Input Parameters

1:     $\mathrm{n}$int64int32nag_int scalar
The number $n$ of variables.
2:     $\mathrm{xc}\left({\mathbf{n}}\right)$ – double array
The point $x$ at which the function and derivatives are required.
3:     $\mathrm{user}$ – Any MATLAB object
funct2 is called from nag_opt_bounds_mod_deriv_easy (e04kz) with the object supplied to nag_opt_bounds_mod_deriv_easy (e04kz).

Output Parameters

1:     $\mathrm{fc}$ – double scalar
The value of the function $F$ at the current point $x$,
2:     $\mathrm{gc}\left({\mathbf{n}}\right)$ – double array
${\mathbf{gc}}\left(\mathit{j}\right)$ must be set to the value of the first derivative $\frac{\partial F}{\partial {x}_{\mathit{j}}}$ at the point $x$, for $\mathit{j}=1,2,\dots ,n$.
3:     $\mathrm{user}$ – Any MATLAB object
3:     $\mathrm{bl}\left({\mathbf{n}}\right)$ – double array
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 ${l}_{1}$; nag_opt_bounds_mod_deriv_easy (e04kz) will then set the remaining elements of bl equal to ${\mathbf{bl}}\left(1\right)$.
4:     $\mathrm{bu}\left({\mathbf{n}}\right)$ – double array
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 ${u}_{1}$; nag_opt_bounds_mod_deriv_easy (e04kz) will then set the remaining elements of bu equal to ${\mathbf{bu}}\left(1\right)$.
5:     $\mathrm{x}\left({\mathbf{n}}\right)$ – double array
${\mathbf{x}}\left(\mathit{j}\right)$ 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 function 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.

### Optional Input Parameters

1:     $\mathrm{n}$int64int32nag_int scalar
Default: the dimension of the arrays bl, bu, x. (An error is raised if these dimensions are not equal.)
The number $n$ of independent variables.
Constraint: ${\mathbf{n}}\ge 1$.
2:     $\mathrm{user}$ – Any MATLAB object
user is not used by nag_opt_bounds_mod_deriv_easy (e04kz), but is passed to funct2. Note that for large objects it may be more efficient to use a global variable which is accessible from the m-files than to use user.

### Output Parameters

1:     $\mathrm{bl}\left({\mathbf{n}}\right)$ – double array
The lower bounds actually used by nag_opt_bounds_mod_deriv_easy (e04kz).
2:     $\mathrm{bu}\left({\mathbf{n}}\right)$ – double array
The upper bounds actually used by nag_opt_bounds_mod_deriv_easy (e04kz).
3:     $\mathrm{x}\left({\mathbf{n}}\right)$ – double array
The lowest point found during the calculations of the position of the minimum.
4:     $\mathrm{f}$ – double scalar
The value of $F\left(x\right)$ corresponding to the final point stored in x.
5:     $\mathrm{g}\left({\mathbf{n}}\right)$ – double array
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.
6:     $\mathrm{user}$ – Any MATLAB object
7:     $\mathrm{ifail}$int64int32nag_int scalar
${\mathbf{ifail}}={\mathbf{0}}$ unless the function detects an error (see Error Indicators and Warnings).

## Error Indicators and Warnings

Note: nag_opt_bounds_mod_deriv_easy (e04kz) may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the function:

Cases prefixed with W are classified as warnings and do not generate an error of type NAG:error_n. See nag_issue_warnings.

${\mathbf{ifail}}=1$
 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 $\mathit{liw}<{\mathbf{n}}+2$, or $\mathit{lw}<\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(10,{\mathbf{n}}×\left({\mathbf{n}}+7\right)\right)$.
${\mathbf{ifail}}=2$
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.
W  ${\mathbf{ifail}}=3$
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 easy-to-use functions.)
W  ${\mathbf{ifail}}=5$
W  ${\mathbf{ifail}}=6$
W  ${\mathbf{ifail}}=7$
W  ${\mathbf{ifail}}=8$
There is some doubt about whether the point $x$ found by nag_opt_bounds_mod_deriv_easy (e04kz) 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 function 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 Further Comments).
${\mathbf{ifail}}=10$
It is very likely that you have made an error in forming the gradient.
${\mathbf{ifail}}=-99$
${\mathbf{ifail}}=-399$
Your licence key may have expired or may not have been installed correctly.
${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.
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 function which uses second derivatives (see the E04 Chapter Introduction).

## Accuracy

When a successful exit is made then, for a computer with a mantissa of $t$ decimals, one would expect to get about $t/2-1$ decimals accuracy in $x$ and about $t-1$ decimals accuracy in $F$, provided the problem is reasonably well scaled.

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 nag_opt_bounds_mod_deriv_easy (e04kz) 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 nag_opt_bounds_mod_deriv_easy (e04kz) will take less computer time.

## Example

A program to minimize
 $F= x1+10x2 2+5⁢ x3-x4 2+ x2-2x3 4+10⁢ x1-x4 4$
subject to
 $1 ≤ x1 ≤ 3 -2 ≤ x2 ≤ 0 1 ≤ x4 ≤ 3$
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.
```function e04kz_example

fprintf('e04kz example results\n\n');

ibound = int64(0);
bl = [ 1; -2; -1000000; 1];
bu = [ 3;  0;  1000000; 3];
x  = [ 3; -1;        0; 1];

% Catch warnings and assume ifail=3,5 gives a good estimate
wstat = warning();
warning('OFF');
[bl, bu, x, f, g, user, ifail] = e04kz(ibound, @funct2, bl, bu, x);
if (ifail == 0 || ifail == 5 | ifail == 3)
fprintf('\nMinimum found at x: ');
fprintf(' %9.4f',x);
fprintf(' %9.4f',g);
fprintf('\nMinimum value     :  %9.4f\n\n',f);
else
fprintf('\n Error: e04kz returns ifail = %d\n',ifail);
end
warning(wstat);

function [fc, gc, user] = funct2(n, xc, user)
gc = zeros(n, 1);
fc = 0;
x1 = xc(1) + 10*xc(2);
x2 = xc(3) -    xc(4);
x3 = xc(2) -  2*xc(3);
x4 = xc(1) -    xc(4);
fc = x1^2 + 5*x2^2 + x3^4 + 10*x4^4;
gc(1) =   2*x1 + 40*x4^3;
gc(2) =  20*x1 +  4*x3^3;
gc(3) =  10*x2 -  8*x3^3;
gc(4) = -10*x2 - 40*x4^3;
```
```e04kz example results

Minimum found at x:     1.0000   -0.0852    0.4093    1.0000
Gradients at x,  g:     0.2953   -0.0000    0.0000    5.9070
Minimum value     :     2.4338

```