Originally posted by: JFYY_yaohua_tang

hello, everyone.

I have a two lp model A and B, model A is a subproblem of model B, which means A have a subset of B's constraint list, but they have the same variable list. Now I have solved model A by CPLEX(C++) and want to test the solution's feasibility to model B, is there any hint?

thankyou.
#CPLEXOptimizers
#DecisionOptimization

Originally posted by: SystemAdmin

If you have the extra constraints of model B stored in some array, then you can use

IloNum IloCplex::getValue(const IloExprArg expr) const;

on model A to evaluate the constraints for the optimal solution of model A.

Tobias
#CPLEXOptimizers
#DecisionOptimization

Originally posted by: SystemAdmin

And if you do not have the constraint stored in an array then use IloModel::Iterator:

bool feasible = true;
for (IloModel::Iterator it(B); it.ok(); ++it) {
  IloExtractable e = *it;
  if (e.isConstraint() && A.getValue(e.asConstraint()) < 0.5) {
    feasible = false;
    break;
  }
}
if (feasible)
  std::cout << "Solution satisfies all constraints in B" << std::endl;
else
  std::cout << "Solution is not feasible for B" << std::endl;

The code-snippet above exploits the fact that you can give an arbitrary constraint as argument to IloCplex::getValue(). The function will return 1 if the current solution satisfies the constraint and 0 otherwise.
#CPLEXOptimizers
#DecisionOptimization

Originally posted by: JFYY_yaohua_tang

I have a ilorangearray for the constraints, but you still teach me something new, thanks very much.
#CPLEXOptimizers
#DecisionOptimization

Originally posted by: JFYY_yaohua_tang

thanks very much, it works quiet very.

a litter question about this function is that it will give me a bool value for the constraint.if conflicts,returns 0 and otherwise 1. Is there any function can directly return the difference between the ilorange.getExpr() and it's bouds?
#CPLEXOptimizers
#DecisionOptimization

Originally posted by: SystemAdmin

IloCplex::getSlack() should do what you want.
#CPLEXOptimizers
#DecisionOptimization

Originally posted by: JFYY_yaohua_tang

thank you.
#CPLEXOptimizers
#DecisionOptimization