NAME
Algorithm::Bertsekas - auction algorithm for the assignment problem.
This is a perl implementation for the auction algorithm for the symmetric allocation problem.
"Both, the auction algorithm and the Kuhn-Munkres algorithm have worst-case time complexity
of (roughly) O(N^3). However, the average-case time complexity of the auction algorithm is
much better. Thus, in practice, with respect to running time, the auction algorithm outperforms
the Kuhn-Munkres (or Hungarian) algorithm significantly."
DESCRIPTION
The assignment problem in the general form can be stated as follows:
"Given N jobs, M tasks and the effectiveness of each job for each task, the problem is to assign each job
to one and only one task in such a way that the measure of effectiveness is optimised (Maximised or Minimised)."
"Each assignment problem has associated with a table or matrix. Generally, the rows contain the jobs or people
we wish to assign, and the columns comprise the tasks or things we want them assigned to. The numbers in the
table are the costs associated with each particular assignment."
One application is to find the (nearest/more distant) neighbors.
The distance between neighbors can be represented by a matrix or a weight function, for example:
1: f(i,j) = abs ($array1[i] - $array2[j])
2: f(i,j) = ($array1[i] - $array2[j]) ** 2
SYNOPSIS
use Algorithm::Bertsekas qw(auction);
my @array1 = ( 64.68, 47.56, 7.36, 80.90, 96.71, 50.10, 44.16 );
my @array2 = ( 3.91, 88.77, 45.56, 79.28 );
my $min = $#array1 < $#array2 ? $#array1 : $#array2;
my $max = $#array1 < $#array2 ? $#array2 : $#array1;
for my $i ( 0 .. $#array1 ){
my @weight_function;
for my $j ( 0 .. $#array2 ){
my $weight = abs ($array1[$i] - $array2[$j]);
# $weight = ($array1[$i] - $array2[$j]) ** 2; # another option
push @weight_function, $weight;
}
push @input_matrix, \@weight_function;
}
3.91 88.77 45.56 79.28
64.68 [ 60.77 24.09 19.12 14.60 ]
47.56 [ 43.65 41.21 2.00 31.72 ]
7.36 [ 3.45 81.41 38.20 71.92 ]
80.90 [ 76.99 7.87 35.34 1.62 ]
96.71 [ 92.80 7.94 51.15 17.43 ]
50.10 [ 46.19 38.67 4.54 29.18 ]
44.16 [ 40.25 44.61 1.40 35.12 ]
@input_matrix = (
[ 60.77, 24.09, 19.12, 14.60 ],
[ 43.65, 41.21, 2.00, 31.72 ],
[ 3.45, 81.41, 38.20, 71.92 ],
[ 76.99, 7.87, 35.34, 1.62 ],
[ 92.80, 7.94, 51.15, 17.43 ],
[ 46.19, 38.67, 4.54, 29.18 ],
[ 40.25, 44.61, 1.40, 35.12 ]
);
my ( $optimal, $assignement_ref, $output_index_ref ) = auction( matrix_ref => \@input_matrix, maximize_total_benefit => 0, verbose => 2 );
Objective: to Minimize the total benefit
Number of left nodes: 7
Number of right nodes: 4
Number of edges: 28
Solution:
Optimal assignment: sum of values = 14.41
Feasible assignment condition: stepsize = 0.2 < 1/4 = 0.25
Maximum index size = [ 0 1 2 3 4 5 6 ]
@output_index indexes = [ 6 5 0 3 1 4 2 ]
@output_index values = [ 3.45 1.62 7.94 1.40 ]
original matrix 7 x 4 with solution:
[ 60.77 24.09 19.12 14.60 ]
[ 43.65 41.21 2.00 31.72 ]
[ 3.45** 81.41 38.20 71.92 ]
[ 76.99 7.87 35.34 1.62**]
[ 92.80 7.94** 51.15 17.43 ]
[ 46.19 38.67 4.54 29.18 ]
[ 40.25 44.61 1.40** 35.12 ]
Pairs (in ascending order of weight function values):
indexes ( 6, 2 ), weight value = 1.40 ; sum of values = 1.40
indexes ( 3, 3 ), weight value = 1.62 ; sum of values = 3.02
indexes ( 2, 0 ), weight value = 3.45 ; sum of values = 6.47
indexes ( 4, 1 ), weight value = 7.94 ; sum of values = 14.41
indexes ( 0, 6 ), weight value = ; sum of values = 14.41
indexes ( 1, 5 ), weight value = ; sum of values = 14.41
indexes ( 5, 4 ), weight value = ; sum of values = 14.41
Common use of the solution:
foreach my $array1_index ( sort { $a <=> $b } keys %{$assignement_ref} ){
my $i = $array1_index;
my $j = $assignement_ref->{$array1_index};
...
}
for my $i (0 .. $max){
my $j = $output_index_ref->[$i];
...
}
assignement hash --> $i = 2 e $value1 = 7.36; $j = 0 e $value2 = 3.91 ; difference = 3.45 ; sum = 3.45
assignement hash --> $i = 3 e $value1 = 80.90; $j = 3 e $value2 = 79.28 ; difference = 1.62 ; sum = 5.07
assignement hash --> $i = 4 e $value1 = 96.71; $j = 1 e $value2 = 88.77 ; difference = 7.94 ; sum = 13.01
assignement hash --> $i = 6 e $value1 = 44.16; $j = 2 e $value2 = 45.56 ; difference = 1.40 ; sum = 14.41
output_index array --> $i = 0 e $value1 = 64.68; $j = 6 e $value2 = ; difference = ; sum = 0.00
output_index array --> $i = 1 e $value1 = 47.56; $j = 5 e $value2 = ; difference = ; sum = 0.00
output_index array --> $i = 2 e $value1 = 7.36; $j = 0 e $value2 = 3.91 ; difference = 3.45 ; sum = 3.45
output_index array --> $i = 3 e $value1 = 80.90; $j = 3 e $value2 = 79.28 ; difference = 1.62 ; sum = 5.07
output_index array --> $i = 4 e $value1 = 96.71; $j = 1 e $value2 = 88.77 ; difference = 7.94 ; sum = 13.01
output_index array --> $i = 5 e $value1 = 50.10; $j = 4 e $value2 = ; difference = ; sum = 13.01
output_index array --> $i = 6 e $value1 = 44.16; $j = 2 e $value2 = 45.56 ; difference = 1.40 ; sum = 14.41
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.
verbose => 3 print messages on the screen, level of verbosity, 0: quiet; 1, 2, 3, 4, 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.
OUTPUT
The $output_index_ref is the reference to the output_index array.
The $assignement_ref is the reference to the assignement hash.
The $optimal is the total benefit which can be a minimum or maximum value.
SEE ALSO
1. Dimitri P. Bertsekas - Network Optimization: Continuous and Discrete Models.
http://web.mit.edu/dimitrib/www/netbook_Full_Book.pdf
2. https://github.com/EvanOman/AuctionAlgorithmCPP/blob/master/auction.cpp
This Perl algorithm has been adapted from this implementation.
3. https://en.wikipedia.org/wiki/Assignment_problem
AUTHOR
Claudio Fernandes de Souza Rodrigues
February 2018
Sao Paulo, Brasil
claudiofsr@yahoo.com
COPYRIGHT AND LICENSE
Copyright (c) 2018 Claudio Fernandes de Souza Rodrigues. All rights reserved.
This program is free software; you can redistribute it and/or modify
it under the same terms as Perl itself.