Algorithm-Bertsekas
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Version 0.84 (Algorithm::Bertsekas-0.84 Released on May 4, 2018)
1. Little changes and improvements.
Version 0.83 (Algorithm::Bertsekas-0.83 Released on May 2, 2018)
1. a hash %this_person_can_choose_n_different_objects was introduced
to increase the assignment number for each round.
Version 0.82 (Algorithm::Bertsekas-0.82 Released on 20th Abr 2018)
1. a variable $max_epsilon_scaling was entered to calculate the
total number of $epsilon_scaling iterations.
Version 0.81 (Algorithm::Bertsekas-0.81 Released on 19th Abr 2018)
1. Little changes and improvements.
Version 0.80 (Algorithm::Bertsekas-0.80 Released on 16th Abr 2018)
1. Little changes and improvements.
Version 0.70 (Algorithm::Bertsekas-0.70 Released on 15th Abr 2018)
1. If $verbose >= 8, print the output to 'auction_output.txt' file.
1. Some changes and improvements.
my ( $optimal, $assignment_ref, $output_index_ref ) = auction( matrix_ref => \@input_matrix, maximize_total_benefit => 0, verbose => 5 );
Objective: to Minimize the total benefit
Number of left nodes: 12
Number of right nodes: 5
Number of edges: 60
Solution:
Optimal assignment: sum of values = 153
Feasible assignment condition: stepsize = 0.1667 < 1/5 = 0.2
Number of iterations: 50
row index = [ 0 1 2 3 4 5 6 7 8 9 10 11 ]
column index = [ 9 8 10 1 5 11 7 4 6 2 0 3 ]
matrix value = [ 17 8 40 36 52 ]
modified matrix 5 x 9:
[ 516 341 150 671 453 719 710 720** 387 ]
[ 598 739** 548 273 661 321 404 322 727 ]
[ 602 427 236 585 539 633 716** 634 473 ]
[ 679 658 467 354 742 402 485 403 704**]
my ( $optimal, $assignment_ref, $output_index_ref ) = auction( matrix_ref => \@input_matrix, maximize_total_benefit => 1, verbose => 3 );
Objective: to Maximize the total benefit
Number of left nodes: 10
Number of right nodes: 10
Number of edges: 100
Solution:
Optimal assignment: sum of values = 893
Feasible assignment condition: stepsize = 0.09091 < 1/10 = 0.1
Number of iterations: 27
row index = [ 0 1 2 3 4 5 6 7 8 9 ]
column index = [ 5 0 1 8 9 6 2 4 7 3 ]
matrix value = [ 95 76 100 90 81 99 99 88 75 90 ]
original matrix 10 x 10 with solution:
[ 84 94 75 56 66 95** 39 53 73 4 ]
[ 76** 71 56 49 29 1 40 40 72 72 ]
[ 85 100** 71 23 47 18 82 70 30 71 ]
[ 2 95 71 89 73 73 48 52 90** 51 ]
lib/Algorithm/Bertsekas.pm view on Meta::CPAN
print "\nObjective: ";
printf( $maximize_total_benefit ? "to Maximize the total benefit\n" : "to Minimize the total benefit\n" );
printf(" Number of left nodes: %u\n", $array1_size );
printf(" Number of right nodes: %u\n", $array2_size );
printf(" Number of edges: %u\n", $array1_size * $array2_size );
print "\nSolution:\n";
printf(" Optimal assignment: sum of values = %.${decimals}f \n", $optimal_benefit );
printf(" Feasible assignment condition: stepsize = %.4g < 1/$min_size = %.4g \n", $epsilon, 1/$min_size ) if ( $verbose >= 1 and $max_size >= 2 );
printf(" Number of iterations: %u \n", $iter_count_global ) if ( $verbose >= 1 );
print "\n row index = [";
for my $i ( 0 .. $#output_index ) {
printf("%${matrix_spaces}d ", $i);
}
print "]\n";
print " column index = [";
for my $index (@output_index) {
printf("%${matrix_spaces}d ", $index);
lib/Algorithm/Bertsekas.pm view on Meta::CPAN
my ( $optimal, $assignment_ref, $output_index_ref ) = auction( matrix_ref => \@input_matrix, maximize_total_benefit => 0, verbose => 5 );
Objective: to Minimize the total benefit
Number of left nodes: 12
Number of right nodes: 5
Number of edges: 60
Solution:
Optimal assignment: sum of values = 153
Feasible assignment condition: stepsize = 0.1667 < 1/5 = 0.2
Number of iterations: 50
row index = [ 0 1 2 3 4 5 6 7 8 9 10 11 ]
column index = [ 9 8 10 1 5 11 7 4 6 2 0 3 ]
matrix value = [ 17 8 40 36 52 ]
modified matrix 5 x 9:
[ 516 341 150 671 453 719 710 720** 387 ]
[ 598 739** 548 273 661 321 404 322 727 ]
[ 602 427 236 585 539 633 716** 634 473 ]
[ 679 658 467 354 742 402 485 403 704**]
lib/Algorithm/Bertsekas.pm view on Meta::CPAN
my ( $optimal, $assignment_ref, $output_index_ref ) = auction( matrix_ref => \@input_matrix, maximize_total_benefit => 1, verbose => 3 );
Objective: to Maximize the total benefit
Number of left nodes: 10
Number of right nodes: 10
Number of edges: 100
Solution:
Optimal assignment: sum of values = 893
Feasible assignment condition: stepsize = 0.09091 < 1/10 = 0.1
Number of iterations: 27
row index = [ 0 1 2 3 4 5 6 7 8 9 ]
column index = [ 5 0 1 8 9 6 2 4 7 3 ]
matrix value = [ 95 76 100 90 81 99 99 88 75 90 ]
original matrix 10 x 10 with solution:
[ 84 94 75 56 66 95** 39 53 73 4 ]
[ 76** 71 56 49 29 1 40 40 72 72 ]
[ 85 100** 71 23 47 18 82 70 30 71 ]
[ 2 95 71 89 73 73 48 52 90** 51 ]
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