This function creates a new constrained integer expression equal to the number of variables in x that are fixed to the value value. This counting constraint has strong relations to the IloDistribute constraint, but is generally easier to use. The following example shows two approaches to creating models with the same solution set.

Using IloCount:

   IloIntVarArray cards(env, 10, 0, 100);
   for (IloInt i = 0; i < 10; i++)
     mdl.add(cards[i] == IloCount(x, i + 1));
 

Using IloDistribute:

   IloIntVarArray cards(env, 10, 0, 100);
   IloIntArray values(env, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
   IloIntVarArray x(env, 100, 1, 10);
   mdl.add(IloDistribute(env, cards, values, x));
 

When an IloCount expression is extracted by CP Optimizer, it is grouped with others counting over the same variable set to provide stronger inference. This means that the same inference strength can be obtained using IloCount in place of IloDistribute.