Mixed-integer linear and nonlinear programming
--- name: mixed-integer-optimization-064c2acf29bd description: Mixed-integer linear and nonlinear programming allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: mathematics domain: science category: optimization phase: 6 graph: domains: [domain:mathematics] specializations: [specialization:computational-mathematics] skillAreas: [skill-area:np-hard-heuristics, skill-area:mathematical-reasoning, skill-area:dynamic-programming] workflows: [workflow:experiment-design] roles: [role:computational-scientist, role:research-engineer] --- # 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--- name: mixed-integer-optimization-064c2acf29bd description: Mixed-integer linear and nonlinear programming allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: mathematics domain: science category: optimization phase: 6 graph: domains: [domain:mathematics] specializations: [specialization:computational-mathematics] skillAreas: [skill-area:np-hard-heuristics, skill-area:mathematical-reasoning, skill-area:dynamic-programming] workflows: [workflow:experiment-design] roles: [role:computational-scientist, role:research-engineer] --- # 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
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