home
***
CD-ROM
|
disk
|
FTP
|
other
***
search
/
CD Concept 6
/
CD Concept 06.iso
/
mac
/
UTILITAIRE
/
RLaB
/
toolbox
/
nmsmax.r
< prev
next >
Wrap
Text File
|
1994-09-23
|
7KB
|
228 lines
#
#NMSMAX [x, fmax, nf] = NMSMAX(fun, x0, STOPIT, SAVIT) attempts to
# maximize the function specified by the string fun, using the
# starting vector x0. The Nelder-Mead direct search method is used.
# Output arguments:
# x = vector yielding largest function value found,
# fmax = function value at x,
# nf = number of function evaluations.
# The iteration is terminated when either
# - the relative size of the simplex is <= STOPIT(1)
# (default 1e-3),
# - STOPIT(2) function evaluations have been performed
# (default inf, i.e., no limit), or
# - a function value equals or exceeds STOPIT(3)
# (default inf, i.e., no test on function values).
# The form of the initial simplex is determined by STOPIT(4):
# STOPIT(4) = 0: regular simplex (sides of equal length, the default)
# STOPIT(4) = 1: right-angled simplex.
# Progress of the iteration is not shown if STOPIT(5) = 0 (default 1).
# If a non-empty fourth parameter string SAVIT is present, then
# `SAVE SAVIT x fmax nf' is executed after each inner iteration.
# NB: x0 can be a matrix. In the output argument, in SAVIT saves,
# and in function calls, x has the same shape as x0.
# References:
# J.E. Dennis, Jr., and D.J. Woods, Optimization on microcomputers:
# The Nelder-Mead simplex algorithm, in New Computing Environments:
# Microcomputers in Large-Scale Computing, A. Wouk, ed., Society for
# Industrial and Applied Mathematics, Philadelphia, 1987, pp. 116-122.
# N.J. Higham, Optimization by direct search in matrix computations,
# Numerical Analysis Report No. 197, University of Manchester, UK, 1991;
# to appear in SIAM J. Matrix Anal. Appl, 14 (2), April 1993.
# This is a heavily modified version of FMINS.M supplied with 386-MATLAB
# version 3.5j.
# By Nick Higham, Department of Mathematics, University of Manchester, UK.
# na.nhigham@na-net.ornl.gov
# July 27, 1991.
# Translated to RLaB, Ian Searle
# Feburary 1994.
nmsmax = function (fun, X, stopit, savit)
{
global (eps)
x = X; # Copy the input
n = prod(size(x));
x0 = x[:]; # Work with column vector internally.
# Set up convergence parameters etc.
if (!exist (stopit)) { stopit[1] = 1e-3; }
tol = stopit[1]; # Tolerance for cgce test based on relative size of simplex.
if (max(size(stopit)) == 1) { stopit[2] = inf(); } # Max no. of f-evaluations.
if (max(size(stopit)) == 2) { stopit[3] = inf(); } # Default target for f-values.
if (max(size(stopit)) == 3) { stopit[4] = 0; } # Default initial simplex.
if (max(size(stopit)) == 4) { stopit[5] = 1; } # Default: show progress.
trace = stopit[5];
if (!exist (savit)) { savit = []; } # File name for snapshots.
V = [zeros(n,1), eye(n,n)];
f = zeros(n+1,1);
V[;1] = x0;
x = reshape (x0, x.nr, x.nc);
f[1] = fun (x);
fmax_old = f[1];
if (trace) { printf("f(x0) = %9.4e\n", f[1]); }
k = 0; m = 0;
# Set up initial simplex.
scale = max([norm(x0,"i"),1]);
if (stopit[4] == 0)
{
# Regular simplex - all edges have same length.
# Generated from construction given in reference [18, pp. 80-81] of [1].
alpha = scale / (n*sqrt(2)) * [ sqrt(n+1)-1+n, sqrt(n+1)-1 ];
V[;2:n+1] = (x0 + alpha[2]*ones(n,1)) * ones(1,n);
for (j in 2:n+1)
{
V[j-1;j] = x0[j-1] + alpha[1];
x = reshape (V[;j], x.nr, x.nc);
f[j] = fun (x);
}
else
# Right-angled simplex based on co-ordinate axes.
alpha = scale*ones(n+1,1);
for (j in 2:n+1)
{
V[;j] = x0 + alpha[j]*V[;j];
x = reshape (V[;j], x.nr, x.nc);
f[j] = fun (x);
}
}
nf = n+1;
how = "initial ";
temp = sort (f).val;
j = sort (f).ind;
j = j[n+1:1:-1];
f = f[j];
V = V[;j];
alpha = 1; beta = 1/2; gamma = 2;
while (1) ###### Outer (and only) loop.
{
k = k+1;
fmax = f[1];
if (fmax > fmax_old)
{
if (!isempty(savit))
{
x = reshape (V[;1], x.nr, x.nc);
write("savit", x, fmax, nf);
}
if (trace)
{
printf("Iter. %2.0f"', k);
printf(" how = %s ", how);
printf("nf = %3.0f, f = %9.4e (%2.1f)\n", nf, fmax, ...
100*(fmax-fmax_old)/(abs(fmax_old)+eps));
}
}
fmax_old = fmax;
### Three stopping tests from MDSMAX.M
# Stopping Test 1 - f reached target value?
if (fmax >= stopit[3])
{
msg = "Exceeded target...quitting\n";
break # Quit.
}
# Stopping Test 2 - too many f-evals?
if (nf >= stopit[3])
{
msg = "Max no. of function evaluations exceeded...quitting\n";
break # Quit.
}
# Stopping Test 3 - converged? This is test (4.3) in [1].
v1 = V[;1];
size_simplex = norm(V[;2:n+1]-v1[;ones(1,n)],"1") / max([1, norm(v1,"1")]);
if (size_simplex <= tol)
{
sprintf(msg, "Simplex size %9.4e <= %9.4e...quitting\n", ...
size_simplex, tol);
break # Quit.
}
# One step of the Nelder-Mead simplex algorithm
# NJH: Altered function calls and changed CNT to NF.
# Changed each `fr < f(1)' type test to `>' for maximization
# and re-ordered function values after sort.
vbar = (sum(V[;1:n]')/n)'; # Mean value
vr = (1 + alpha)*vbar - alpha*V[;n+1];
x = reshape (vr, x.nr, x.nc);
fr = fun (x);
nf = nf + 1;
vk = vr; fk = fr; how = "reflect, ";
if (fr > f[n])
{
if (fr > f[1])
{
ve = gamma*vr + (1-gamma)*vbar;
x = reshape (ve, x.nr, x.nc);
fe = fun (x);
nf = nf + 1;
if (fe > f[1])
{
vk = ve; fk = fe;
how = "expand, ";
}
}
else
vt = V[;n+1];
ft = f[n+1];
if (fr > ft)
{
vt = vr;
ft = fr;
}
vc = beta*vt + (1-beta)*vbar;
x = reshape (vc, x.nr, x.nc);
fc = fun (x);
nf = nf + 1;
if (fc > f[n])
{
vk = vc; fk = fc;
how = "contract,";
else
for (j in 2:n)
{
V[;j] = (V[;1] + V[;j])/2;
x = reshape (V[;j], x.nr, x.nc);
f[j] = fun (x);
}
nf = nf + n-1;
vk = (V[;1] + V[;n+1])/2;
x = reshape (vk, x.nr, x.nc);
fk = fun (x);
nf = nf + 1;
how = "shrink, ";
}
}
V[;n+1] = vk;
f[n+1] = fk;
temp = sort(f).val;
j = sort (f).ind;
j = j[n+1:1:-1];
f = f[j];
V = V[;j];
} ###### End of outer (and only) loop.
# Finished.
if (trace) { printf(msg); }
x = reshape (V[;1], x.nr, x.nc);
return <<x = x; fmax = fmax; nf = nf>>;
};