NAG Library Routine Document
E04CBF
1 Purpose
E04CBF minimizes a general function
$F\left(\mathbf{x}\right)$ of
$n$ independent variables
$\mathbf{x}={\left({x}_{1},{x}_{2},\dots ,{x}_{n}\right)}^{\mathrm{T}}$ by the Nelder and Mead simplex method (see
Nelder and Mead (1965)). Derivatives of the function need not be supplied.
2 Specification
SUBROUTINE E04CBF ( 
N, X, F, TOLF, TOLX, FUNCT, MONIT, MAXCAL, IUSER, RUSER, IFAIL) 
INTEGER 
N, MAXCAL, IUSER(*), IFAIL 
REAL (KIND=nag_wp) 
X(N), F, TOLF, TOLX, RUSER(*) 
EXTERNAL 
FUNCT, MONIT 

3 Description
E04CBF finds an approximation to a minimum of a function $F$ of $n$ variables. You must supply a subroutine to calculate the value of $F$ for any set of values of the variables.
The method is iterative. A simplex of
$n+1$ points is set up in the
$n$dimensional space of the variables (for example, in
$2$ dimensions the simplex is a triangle) under the assumption that the problem has been scaled so that the values of the independent variables at the minimum are of order unity. The starting point you have provided is the first vertex of the simplex, the remaining
$n$ vertices are generated by E04CBF. The vertex of the simplex with the largest function value is reflected in the centre of gravity of the remaining vertices and the function value at this new point is compared with the remaining function values. Depending on the outcome of this test the new point is accepted or rejected, a further expansion move may be made, or a contraction may be carried out. See
Nelder and Mead (1965) and
Parkinson and Hutchinson (1972) for more details. When no further progress can be made the sides of the simplex are reduced in length and the method is repeated.
The method can be slow, but computational bottlenecks have been reduced following
Singer and Singer (2004). However, E04CBF is robust, and therefore very useful for functions that are subject to inaccuracies.
There are the following options for successful termination of the method: based only on the function values at the vertices of the current simplex (see
(1)); based only on a volume ratio between the current simplex and the initial one (see
(2)); or based on which one of the previous two tests passes first. The volume test may be useful if
$F$ is discontinuous, while the functionvalue test should be sufficient on its own if
$F$ is continuous.
4 References
Nelder J A and Mead R (1965) A simplex method for function minimization Comput. J. 7 308–313
Parkinson J M and Hutchinson D (1972) An investigation into the efficiency of variants of the simplex method Numerical Methods for Nonlinear Optimization (ed F A Lootsma) Academic Press
Singer S and Singer S (2004) Efficient implementation of the Nelder–Mead search algorithm Appl. Num. Anal. Comp. Math. 1(3) 524–534
5 Arguments
 1: $\mathrm{N}$ – INTEGERInput

On entry: $n$, the number of variables.
Constraint:
${\mathbf{N}}\ge 1$.
 2: $\mathrm{X}\left({\mathbf{N}}\right)$ – REAL (KIND=nag_wp) arrayInput/Output

On entry: a guess at the position of the minimum. Note that the problem should be scaled so that the values of the ${\mathbf{X}}\left(i\right)$ are of order unity.
On exit: the value of
$\mathbf{x}$ corresponding to the function value in
F.
 3: $\mathrm{F}$ – REAL (KIND=nag_wp)Output

On exit: the lowest function value found.
 4: $\mathrm{TOLF}$ – REAL (KIND=nag_wp)Input

On entry: the error tolerable in the function values, in the following sense. If
${f}_{\mathit{i}}$, for
$\mathit{i}=1,2,\dots ,n+1$, are the individual function values at the vertices of the current simplex, and if
${f}_{m}$ is the mean of these values, then you can request that E04CBF should terminate if
You may specify
${\mathbf{TOLF}}=0$ if you wish to use only the termination criterion
(2) on the spatial values: see the description of
TOLX.
Constraint:
${\mathbf{TOLF}}$ must be greater than or equal to the
machine precision (see
Chapter X02), or if
TOLF equals zero then
TOLX must be greater than or equal to the
machine precision.
 5: $\mathrm{TOLX}$ – REAL (KIND=nag_wp)Input

On entry: the error tolerable in the spatial values, in the following sense. If
$LV$ denotes the ‘linearized’ volume of the current simplex, and if
${LV}_{\mathrm{init}}$ denotes the ‘linearized’ volume of the initial simplex, then you can request that E04CBF should terminate if
You may specify
${\mathbf{TOLX}}=0$ if you wish to use only the termination criterion
(1) on function values: see the description of
TOLF.
Constraint:
${\mathbf{TOLX}}$ must be greater than or equal to the
machine precision (see
Chapter X02), or if
TOLX equals zero then
TOLF must be greater than or equal to the
machine precision.
 6: $\mathrm{FUNCT}$ – SUBROUTINE, supplied by the user.External Procedure

FUNCT must evaluate the function
$F$ at a specified point. It should be tested separately before being used in conjunction with E04CBF.
The specification of
FUNCT is:
INTEGER 
N, IUSER(*) 
REAL (KIND=nag_wp) 
XC(N), FC, RUSER(*) 

 1: $\mathrm{N}$ – INTEGERInput

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

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

On exit: the value of the function $F$ at the current point $\mathbf{x}$.
 4: $\mathrm{IUSER}\left(*\right)$ – INTEGER arrayUser Workspace
 5: $\mathrm{RUSER}\left(*\right)$ – REAL (KIND=nag_wp) arrayUser Workspace

FUNCT is called with the arguments
IUSER and
RUSER as supplied to E04CBF. You should use the arrays
IUSER and
RUSER to supply information to
FUNCT.
FUNCT must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which E04CBF is called. Arguments denoted as
Input must
not be changed by this procedure.
 7: $\mathrm{MONIT}$ – SUBROUTINE, supplied by the NAG Library or the user.External Procedure

MONIT may be used to monitor the optimization process. It is invoked once every iteration.
If no monitoring is required,
MONIT may be the dummy monitoring routine E04CBK supplied by the NAG Library.
The specification of
MONIT is:
INTEGER 
N, NCALL, IUSER(*) 
REAL (KIND=nag_wp) 
FMIN, FMAX, SIM(N+1,N), SERROR, VRATIO, RUSER(*) 

 1: $\mathrm{FMIN}$ – REAL (KIND=nag_wp)Input

On entry: the smallest function value in the current simplex.
 2: $\mathrm{FMAX}$ – REAL (KIND=nag_wp)Input

On entry: the largest function value in the current simplex.
 3: $\mathrm{SIM}\left({\mathbf{N}}+1,{\mathbf{N}}\right)$ – REAL (KIND=nag_wp) arrayInput

On entry: the $n+1$ position vectors of the current simplex.
 4: $\mathrm{N}$ – INTEGERInput

On entry: $n$, the number of variables.
 5: $\mathrm{NCALL}$ – INTEGERInput

On entry: the number of times that
FUNCT has been called so far.
 6: $\mathrm{SERROR}$ – REAL (KIND=nag_wp)Input

On entry: the current value of the standard deviation in function values used in termination test
(1).
 7: $\mathrm{VRATIO}$ – REAL (KIND=nag_wp)Input

On entry: the current value of the linearized volume ratio used in termination test
(2).
 8: $\mathrm{IUSER}\left(*\right)$ – INTEGER arrayUser Workspace
 9: $\mathrm{RUSER}\left(*\right)$ – REAL (KIND=nag_wp) arrayUser Workspace

MONIT is called with the arguments
IUSER and
RUSER as supplied to E04CBF. You should use the arrays
IUSER and
RUSER to supply information to
MONIT.
MONIT must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which E04CBF is called. Arguments denoted as
Input must
not be changed by this procedure.
 8: $\mathrm{MAXCAL}$ – INTEGERInput

On entry: the maximum number of function evaluations to be allowed.
Constraint:
${\mathbf{MAXCAL}}\ge 1$.
 9: $\mathrm{IUSER}\left(*\right)$ – INTEGER arrayUser Workspace
 10: $\mathrm{RUSER}\left(*\right)$ – REAL (KIND=nag_wp) arrayUser Workspace

IUSER and
RUSER are not used by E04CBF, but are passed directly to
FUNCT and
MONIT and should be used to pass information to these routines.
 11: $\mathrm{IFAIL}$ – 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, if you are not familiar with this argument, the recommended value is
$0$.
When the value $\mathbf{1}\text{ or}\mathbf{1}$ 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
If on entry
${\mathbf{IFAIL}}=0$ or
$1$, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
Errors or warnings detected by the routine:
 ${\mathbf{IFAIL}}=1$

On entry, ${\mathbf{MAXCAL}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{MAXCAL}}\ge 1$.
On entry, ${\mathbf{N}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{N}}\ge 1$.
On entry, ${\mathbf{TOLF}}=0.0$ and ${\mathbf{TOLX}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: if ${\mathbf{TOLF}}=0.0$ then ${\mathbf{TOLX}}$ is greater than or equal to the machine precision.
On entry, ${\mathbf{TOLF}}=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{TOLX}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: if ${\mathbf{TOLF}}\ne 0.0$ and ${\mathbf{TOLX}}\ne 0.0$ then both should be greater than or equal to the machine precision.
On entry, ${\mathbf{TOLX}}=0.0$ and ${\mathbf{TOLF}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: if ${\mathbf{TOLX}}=0.0$ then ${\mathbf{TOLF}}$ is greater than or equal to the machine precision.
 ${\mathbf{IFAIL}}=2$

MAXCAL function evaluations have been completed without any other termination test passing. Check the coding of
FUNCT before increasing the value of
MAXCAL.
 ${\mathbf{IFAIL}}=99$
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.
7 Accuracy
On a successful exit the accuracy will be as defined by
TOLF or
TOLX, depending on which criterion was satisfied first.
8 Parallelism and Performance
E04CBF is not threaded in any implementation.
Local workspace arrays of fixed lengths (depending on
N) are allocated internally by E04CBF. The total size of these arrays amounts to
${{\mathbf{N}}}^{2}+6{\mathbf{N}}+2$ real elements.
The time taken by E04CBF depends on the number of variables, the behaviour of the function and the distance of the starting point from the minimum. Each iteration consists of $1$ or $2$ function evaluations unless the size of the simplex is reduced, in which case $n+1$ function evaluations are required.
10 Example
This example finds a minimum of the function
This example uses the initial point
$\left(1,1\right)$ (see
Section 10.3), and we expect to reach the minimum at
$\left(0.5,1\right)$.
10.1 Program Text
Program Text (e04cbfe.f90)
10.2 Program Data
None.
10.3 Program Results
Program Results (e04cbfe.r)