5. Modeling and Optimization

In this chapter we discuss how to use MindOpt to solve mathematical optimization problems. We provide a series of examples on constructing different types of optimziation models, and calling the optimization solvers to obtain the optimal solutions.

Note

Currently MindOpt only supports linear programming (LP), mixed-integer linear programming (MILP), convex quadratic programming (QP) and semidefinite programming (SDP). In the future, MindOpt will support more problem types and continuously improve its performance.

• 5.1. Linear Programming (LP)
  • 5.1.1. Modeling for Linear Programming
  • 5.1.2. LP Modeling and Optimization in C
  • 5.1.3. LP Modeling and Optimization in C++
  • 5.1.4. LP Modeling and Optimization in Python
  • 5.1.5. LP Modeling and Optimization in JAVA
• 5.2. Mixed-Integer Linear Programming (MILP)
  • 5.2.1. Modeling for Mixed-Integer Linear Programming
  • 5.2.2. MILP Modeling and Optimization in C
  • 5.2.3. MILP Modeling and Optimization in C++
  • 5.2.4. MILP Modeling and Optimization in Python
  • 5.2.5. MILP Modeling and Optimization in JAVA
  • 5.2.6. MILP Warmstart
  • 5.2.7. General Constraints in MILP
• 5.3. Quadratic Programming (QP)
  • 5.3.1. Modeling for Quadratic Programming
  • 5.3.2. QP Modeling and Optimization in C
  • 5.3.3. QP Modeling and Optimization in C++
  • 5.3.4. QP Modeling and Optimization in Python
• 5.4. Semi-Definite Programming (SDP)
  • 5.4.1. Modeling for Semi-Definite Programming
  • 5.4.2. SDP Modeling and Optimization in C
  • 5.4.3. SDP Modeling and Optimization in C++
  • 5.4.4. SDP Modeling and Optimization in Python