| Math-Amoeba documentation | Contained in the Math-Amoeba distribution. |
Math::Amoeba - Multidimensional Function Minimisation
use Math::Amoeba qw(ConstructVertices EvaluateVertices Amoeba MinimiseND);
my ($vertice,$y)=MinimiseND(\@guess,\@scales,\&func,$tol,$itmax,$verbose);
my @vertices=ConstructVertices(\@vector,\@offsets);
my @y=EvaluateVertices(\@vertices,\&func);
my ($vertice,$y)=Amoeba(\@vertices,\@y,\&func,$tol,$itmax,$verbose);
This is an implimenation of the Downhill Simpex Method in Multidimensions (Nelder and Mead) for finding the (local) minimum of a function. Doing this in Perl makes it easy for that function to actually be the output of another program such as a simulator.
Arrays and the function are passed by reference to the routines.
MinimiseNDThe simplest use is the MinimiseND function. This takes a reference to an array of guess values for the parameters at the function minimum, a reference to an array of scales for these parameters (sensible ranges around the guess in which to look), a reference to the function, a convergence tolerence for the minimum, the maximum number of iterations to be taken and the verbose flag (default ON). It returns an array consisting of a reference to the function parameters at the minimum and the value there.
AmoebaThe Amoeba function is the actual implimentation of the Downhill Simpex Method in Multidimensions. It takes a reference to an array of references to arrays which are the initial n+1 vertices (where n is the number of function parameters), a reference to the function valuation at these vertices, a reference to the function, a convergence tolerence for the minimum, the maximum number of iterations to be taken and the verbose flag (default ON). It returns an array consisting of a reference to the function parameters at the minimum and the value there.
ConstructVerticesThe ConstructVertices is used by MinimiseND to construct the initial vertices for Amoeba as the initial guess plus the parameter scale parameters as vectors along the parameter axis.
EvaluateVerticesThe EvaluateVertices takes these set of vertices, calling the function for each one and returning the vector of results.
use Math::Amoeba qw(MinimiseND);
sub afunc {
my ($a,$b)=@_;
print "$a\t$b\n";
return ($a-7)**2+($b+3)**2;
}
my @guess=(1,1);
my @scale=(1,1);
($p,$y)=MinimiseND(\@guess,\@scale,\&afunc,1e-7,100);
print "(",join(',',@{$p}),")=$y\n";
produces the output
(6.99978191653352,-2.99981241563247)=1.00000008274829
PERL
See "REAME".
Let me know.
John A.R. Williams <J.A.R.Williams@aston.ac.uk>
Tom Chau <tom@cpan.org>
"Numerical Recipies: The Art of Scientific Computing" W.H. Press, B.P. Flannery, S.A. Teukolsky, W.T. Vetterling. Cambridge University Press. ISBN 0 521 30811 9.
Copyright (c) 1995 John A.R. Williams. All rights reserved. This program is free software; you can redistribute it and/or modify it under the same terms as Perl itself.
Since 2005, this module was co-developed with Tom.
| Math-Amoeba documentation | Contained in the Math-Amoeba distribution. |
# This library file contains Amoeba n-D Minimisation routine for Perl # $Id: Amoeba.pm,v 1.2 1995/12/24 12:37:46 willijar Exp $ # $Id: Amoeba.pm,v 1.2 1995/12/24 12:37:46 willijar Exp $ package Math::Amoeba; use strict; use warnings; our $VERSION = 0.05; use Carp; use constant TINY => 1e-16; use Exporter; our @ISA=qw(Exporter); our @EXPORT_OK=qw(ConstructVertices EvaluateVertices Amoeba MinimiseND);
my ($ALPHA,$BETA,$GAMMA)=(1.0,0.5,2.0); sub MinimiseND { my ($guesses,$scales,$func,$tol,$itmax, $verbose)=@_; my @p=ConstructVertices($guesses,$scales); my @y=EvaluateVertices(\@p,$func); return Amoeba(\@p,\@y,$func,$tol,$itmax, $verbose); } sub ConstructVertices { # given 2 vector references constructs an amoeba # returning the vertices my ($vector,$ofs)=@_; my $n=$#{$vector}; my @vector=@{$vector}; my (@p,@y,$i); $p[0]=[]; @{$p[0]}=@{$vector}; for($i=0; $i<=$n; $i++) { my $v=[]; @{$v}=@{$vector}; $v->[$i]+=$ofs->[$i]; $p[$i+1]=$v; } return @p; } sub EvaluateVertices { # evaluates functions for all vertices of the amoeba my ($p,$func)=@_; my ($i,@y); for($i=0; $i<=$#{$p}; $i++) { $y[$i]=&$func(@{$p->[$i]}); } return @y; } sub Amoeba { my ($p,$y,$func,$ftol,$itmax, $verbose)=@_; my $n=$#{$p}; # no points # Default parameters $verbose = (defined($verbose)) ? $verbose : 1; if (!$itmax) { $itmax=200; } if (!$ftol) { $ftol=1e-6; } # Member variables my ($i,$j); my $iter=0; my ($ilo, $inhi, $ihi); my ($pbar, $pr, $pe, $pc, $ypr, $ype, $ypc); # To control the recalculation of centroid my $recalc = 1; my $ihi_o; # Loop until any of stopping conditions hit while (1) { ($ilo, $inhi, $ihi) = _FindMarkers($y); # Stopping conditions my $rtol = 2*abs($y->[$ihi]-$y->[$ilo])/(abs($y->[$ihi])+abs($y->[$ilo])+TINY); if ($rtol<$ftol) { last; } if ($iter++>$itmax) { carp "Amoeba exceeded maximum iterations\n" if ($verbose); last; } # Determine the Centroid if ($recalc) { $pbar = _CalcCentroid($p, $ihi); } else { _AdjustCentroid($pbar, $p, $ihi_o, $ihi); } # Reset the re-calculation flag, and remember the current highest $recalc = 0; # Determine the reflection point, evaluate its value $pr = _CalcReflection($pbar, $p->[$ihi], $ALPHA); $ypr = &$func(@$pr); # if it gives a better value than best point, try an # additional extrapolation by a factor gamma, accept best if ($ypr < $y->[$ilo]) { $pe = _CalcReflection($pbar, $pr, -$GAMMA); $ype=&$func(@$pe); if ($ype < $y->[$ilo]) { $p->[$ihi] = $pe; $y->[$ihi] = $ype; } else { $p->[$ihi] = $pr; $y->[$ihi] = $ypr; } } # if reflected point worse than 2nd highest elsif ($ypr >= $y->[$inhi]) { # if it is better than highest, replace it if ($ypr < $y->[$ihi] ) { $p->[$ihi] = $pr; $y->[$ihi] = $ypr; } # look for an intermediate lower point by performing a # contraction of the simplex along one dimension $pc = _CalcReflection($pbar, $p->[$ihi], -$BETA); $ypc = &$func(@$pc); # if contraction gives an improvement, accept it if ($ypc < $y->[$ihi]) { $p->[$ihi] = $pc; $y->[$ihi] = $ypc; } # otherwise cant seem to remove high point # so contract around lo (best) point else { for($i=0; $i<=$n; $i++) { if ($i!=$ilo) { $p->[$i] = _CalcReflection($p->[$ilo], $p->[$i], -$BETA); $y->[$i] = &$func(@{$p->[$i]}); } } $recalc = 1; } } # if reflected point is in-between lowest and 2nd highest else { $p->[$ihi] = $pr; $y->[$ihi] = $ypr; } # Remember the replacing position and its value $ihi_o = $ihi; } return ($p->[$ilo],$y->[$ilo]); } # Helper function - find the lowest, 2nd highest and highest position sub _FindMarkers { my $y = shift; my ($ilo, $inhi, $ihi); my ($i, $n); $n = @$y - 1; $ilo=0; if ($y->[0]>$y->[1]) { $ihi=0; $inhi=1; } else { $ihi=1; $inhi=0; } for($i = 0; $i <= $n; $i++) { if ($y->[$i] < $y->[$ilo]) { $ilo = $i; } if ($y->[$i] > $y->[$ihi]) { $inhi = $ihi; $ihi = $i; } elsif ($y->[$i] > $y->[$inhi] && $ihi != $i) { $inhi = $i; } } return ($ilo, $inhi, $ihi); } # Determine the centroid (except the highest point) sub _CalcCentroid { my ($p, $ihi) = @_; my ($i, $j, $n); $n = @$p - 1; my $pbar = []; for($j=0; $j<$n; $j++) { for($i=0; $i<=$n; $i++) { if ($i!=$ihi) { $pbar->[$j] += $p->[$i][$j]; } } $pbar->[$j] /= $n; } return $pbar; } # Adjust the centroid only sub _AdjustCentroid { my ($pbar, $p, $ihi_o, $ihi) = @_; my ($j, $n); $n = @$pbar; if ($ihi_o != $ihi) { for($j=0; $j<$n; $j++) { $pbar->[$j] += ($p->[$ihi_o][$j] - $p->[$ihi][$j]) / $n; } } } # Determine the reflection point sub _CalcReflection { my ($p1, $p2, $scale) = @_; my $j; my $n = @$p1; my $pr = []; for($j=0; $j<$n; $j++) { $pr->[$j] = $p1->[$j] + $scale*($p1->[$j]-$p2->[$j]); } return $pr; } return 1; __END__