name: mixed-integer-optimization description: Mixed-integer linear and nonlinear programming allowed-tools:

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• Grep metadata: specialization: mathematics domain: science category: optimization phase: 6

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

1. Formulation: Use tight formulations with valid inequalities
2. Big-M Selection: Choose appropriate Big-M values
3. Branching: Configure branching priorities
4. Solution Pool: Generate diverse feasible solutions

Tools/Libraries

• Gurobi
• CPLEX
• SCIP
• CBC