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lib/Math/Amoeba.pm  view on Meta::CPAN

=over

=item C<MinimiseND>

The simplest use is the B<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.

=item C<Amoeba>

The B<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.

=item C<ConstructVertices>

The B<ConstructVertices> is used by B<MinimiseND> to construct the
initial vertices for B<Amoeba> as the initial guess plus the parameter
scale parameters as vectors along the parameter axis.

=item C<EvaluateVertices>

lib/Math/Amoeba.pm  view on Meta::CPAN

	# 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);
		}