MIP solvers like Gurobi Optimizer solve integer programs via a series of linear programming relaxations. A solution is deemed integer if all integer variables are within a tolerance value (IntFeasTol) of an integer solution. The default value of IntFeasTol is 1e-5, so a value like 1.000000465 would be considered an integer solution. Gurobi Optimizer does not provide a rounding feature because in many models, rounding to an exact integer value can create problems, such as making the solution infeasible.
Articles in this section
- What is the difference between user cuts and lazy constraints?
- Why do I see increasing/large MIP gap values?
- How do you implement lazy constraints in Gurobi?
- Can you modify the branch-and-bound algorithm or create a branch-cut-and-price algorithm?
- Does Gurobi have a solution polishing algorithm?
- How do I find additional solutions to a model?
- Does the barrier algorithm return a basic solution for LPs?
- When will more threads make it faster to solve a model?
- Why does Gurobi sometimes return values for integer variables that are not integers?