This tutorial was written by Michael Schmitt, a student of the course “Discrete Optimization”.

Branch & Bound

In this Tutorial, I like to describe, how one can implement the Branch & Bound Algorithm for polytopes using polymake.


This preamble must be integrated in every script file, so that we can use the polytope functionalities of polymake.

use application "polytope";

At first we create some global variables that keep track over the best integer solution, that has been discovered so far.

$::z = -10000;


I introduce a subroutine, that returns the non-integer part of some rational number.

sub f {
    my $x=shift;
    my $denom = denominator($x);
    my $nom = convert_to<Integer>($denom * $x);
    my $remainder = $nom % $denom;
    if($remainder < 0){
      $remainder += $denom;
    return new Rational($remainder, $denom);

Main routine

It follows the main routine, that will call itself recursively. The parameters that are passed to this function are:

  • The objective function as an Vector<Rational>
  • A list of inequalities that define the currently examined polytope
sub branchnbound {

my $p = new Array<Rational>;

The following line gets the parameters.

my $objective = shift;
my @ineqs = @_;

my $mat = new Matrix<Rational>(@ineqs);
#print "Current Inequalities: \n", $mat, "\n";

$p->INEQUALITIES = $mat;

Feasibility check

Let's check, whether the polytope is empty or not. If it is empty, we can stop examining this branch of the B&B Tree.

    print "EMPTY!\n";

Solving the relaxation

We look for the optimal solution of the linear relaxation.

$p->LP = new LinearProgram<Rational>(LINEAR_OBJECTIVE=>$objective);

my $x = $p->LP->MAXIMAL_VERTEX;
my $ctx = $p->LP->MAXIMAL_VALUE;

print "Relaxed solution: ", $x, " -> ", $ctx,"\n";


And if that is already worse then the best integer solution so far, we can prune this branch.

# Pruning 
if($ctx <= $::z){
    "is pruned, because <= $::z\n";
    return 0;

Check for integer solution

In the following loop, we find the first coordinate of the solution vector, that is not integer.

my $i = 0;
for(my $k=1; $k<$x->dim(); $k++){
    if(f($x->[$k]) != 0){
        $i = $k;

If we didn't find any, we have found an integer solution and possibly a new best solution. In the case of an integer solution, we can stop here.

if($i == 0){
    print "Solution is Integer.\n";
    # Update the best solution
    if($ctx > $::z){
        $::x_best = $x;
        $::z = $ctx;
        print "Solution is better then the last optimum.\n";


Now lets create some new inequalities. At first round the not integer coordinate of x up and down.

#Round up and down:
my $x_i_down = $x->[$i] - f($x->[$i]);
my $x_i_up = $x_i_down + 1;

To generate P_1, add the inequality x_i ⇐ x_i_down.

my @ineq_to_add_1 = ($x_i_down);
for (my $k = 1; $k<$x->dim(); $k++){
    if($k == $i){
        push @ineq_to_add_1, -1;
        push @ineq_to_add_1, 0;

To generate P_2, add the inequality x_i >= x_i_up.

my @ineq_to_add_2 = (-$x_i_up);
for (my $k = 1; $k<$x->dim(); $k++){
    if($k == $i){
        push @ineq_to_add_2, 1;
        push @ineq_to_add_2, 0;

Now add the new inequalities to the old ones …

@ineq_to_add_1 = new Array<Rational>(@ineq_to_add_1);
@ineq_to_add_2 = new Array<Rational>(@ineq_to_add_2);

my @new_ineqs_1 = @ineqs;
push @new_ineqs_1, @ineq_to_add_1;

my @new_ineqs_2 = @ineqs;
push @new_ineqs_2, @ineq_to_add_2;

And do the recursive calls.

Here I added some printouts, that give an inorder traversal of the B&B tree and the edges that are used for that.

print "Set x$i <= $x_i_down\n";
print "Forget x$i <= $x_i_down\n";

print "Set x$i >= $x_i_up\n";
print "Forget x$i >= $x_i_up\n";


Finally, the first call of the Routine:

my @ineqs = ([14,-7,2],[3,0,-1],[3,-2,2],[0,1,0],[0,0,1]);
my $objective = [0,4,-1];
branchnbound($objective, @ineqs);

print "The solution is: $::x_best with the optimal value $::z\n";
tutorial/michaels_tutorial.txt · Last modified: 2014/01/03 15:45 (external edit)
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