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README  view on Meta::CPAN

   indexes (  5,  6 ), matrix value =  99 ; sum of values = 694
   indexes (  6,  2 ), matrix value =  99 ; sum of values = 793
   indexes (  2,  1 ), matrix value = 100 ; sum of values = 893
 
OPTIONS
 
 matrix_ref => \@input_matrix,   reference to array: matrix N x M.
 maximize_total_benefit => 0,    0: minimize the total benefit ; 1: maximize the total benefit.
 inicial_stepsize       => 1,    auction algorithm terminates with a feasible assignment if the problem data are integer and stepsize < 1/min(N,M).
 inicial_price          => 0,			
 verbose                => 3,    print messages on the screen, level of verbosity, 0: quiet; 1, 2, 3, 4, 5, 8, 9, 10: debug information.

EXPORT

    "auction" function by default.

INPUT

    The input matrix should be in a two dimensional array (array of array) 
	and the 'auction' subroutine expects a reference to this array.

lib/Algorithm/Bertsekas.pm  view on Meta::CPAN

my ( $need_transpose, $inicial_price, $iter_count_global, $iter_count_local );
my ( $epsilon_scaling, $max_epsilon_scaling, $max_matrix_value, $target, $output );
my ( %index_correlation, %assignned_object, %assignned_person, %price_object );
my ( %objects_desired_by_this, %locked_list, %seen_person, %seen_assignned_objects );

sub auction { #						=> default values
	my %args = ( matrix_ref				=> undef,     # reference to array: matrix N x M			                                     
				maximize_total_benefit  => 0,         # 0: minimize_total_benefit ; 1: maximize_total_benefit
				inicial_stepsize        => undef,     # auction algorithm terminates with a feasible assignment if the problem data are integer and stepsize < 1/min(N,M)				
				inicial_price           => 0,
				verbose                 => 3,         # level of verbosity, 0: quiet; 1, 2, 3, 4, 5, 8, 9, 10: debug information.
				@_,                                   # argument pair list goes here
				);
       
	$max_matrix_value = 0;
	$iter_count_global = 0;
	$epsilon_scaling = 0;
	$need_transpose = 0;
	%index_correlation = ();
	%assignned_object = ();
	%assignned_person = ();

lib/Algorithm/Bertsekas.pm  view on Meta::CPAN

   indexes (  5,  6 ), matrix value =  99 ; sum of values = 694
   indexes (  6,  2 ), matrix value =  99 ; sum of values = 793
   indexes (  2,  1 ), matrix value = 100 ; sum of values = 893
 
=head1 OPTIONS
 
 matrix_ref => \@input_matrix,   reference to array: matrix N x M.
 maximize_total_benefit => 0,    0: minimize the total benefit ; 1: maximize the total benefit.
 inicial_stepsize       => 1,    auction algorithm terminates with a feasible assignment if the problem data are integer and stepsize < 1/min(N,M).
 inicial_price          => 0,			
 verbose                => 3,    print messages on the screen, level of verbosity, 0: quiet; 1, 2, 3, 4, 5, 8, 9, 10: debug information.

=head1 EXPORT

    "auction" function by default.

=head1 INPUT

    The input matrix should be in a two dimensional array (array of array) 
	and the 'auction' subroutine expects a reference to this array.