Mixed-Integer Optimization
Purpose
Provides capabilities for formulating and solving mixed-integer linear and nonlinear programming problems.
Capabilities
- Branch and bound/cut algorithms
- MIP formulation techniques
- Indicator constraints
- Big-M reformulations
- Lazy constraints
- Solution pool generation
Usage Guidelines
- Formulation: Use tight formulations with valid inequalities
- Big-M Selection: Choose appropriate Big-M values
- Branching: Configure branching priorities
- Solution Pool: Generate diverse feasible solutions
Tools/Libraries
- Gurobi
- CPLEX
- SCIP
- CBC