MindOpt
0.25.0

Contents:

  • 1. Quick start
  • 2. Overview
    • 2.1. Introduction
    • 2.2. Changelog
      • 2.2.1. MindOpt 0.25.0
      • 2.2.2. MindOpt 0.24.1
      • 2.2.3. MindOpt 0.24.0
      • 2.2.4. MindOpt 0.23.1
      • 2.2.5. MindOpt 0.23.0
      • 2.2.6. Previous versions
    • 2.3. License agreement
      • 2.3.1. MindOpt Solver
      • 2.3.2. ZLIB
      • 2.3.3. BZLIB2
      • 2.3.4. Intel MKL
      • 2.3.5. Sphinx
      • 2.3.6. JSON
      • 2.3.7. Cereal
      • 2.3.8. CURL
      • 2.3.9. TFlite
  • 3. Installation
    • 3.1. Supported platforms
    • 3.2. Folder structure
    • 3.3. Download MindOpt
    • 3.4. Installation instructions
      • 3.4.1. Installation on Windows
      • 3.4.2. Installation on Linux
      • 3.4.3. Installation on OSX
    • 3.5. License settings
      • 3.5.1. Obtain License
      • 3.5.2. Installation on Windows
      • 3.5.3. Installation on Linux
      • 3.5.4. Installation on OSX
  • 4. Call methods
    • 4.1. Commandline interface
      • 4.1.1. Brief description
      • 4.1.2. Example
      • 4.1.3. Input file format: mps/lp/dat-s
      • 4.1.4. Input parameters
      • 4.1.5. Sanitize Model Files
    • 4.2. Using C language
      • 4.2.1. Brief description
      • 4.2.2. Windows
      • 4.2.3. Linux
      • 4.2.4. OSX
      • 4.2.5. C compilation example: MdoMps
    • 4.3. Using C++
      • 4.3.1. Brief description
      • 4.3.2. Windows
      • 4.3.3. Linux
      • 4.3.4. OSX
      • 4.3.5. C++ compilation example: MdoMps
    • 4.4. Using Python
      • 4.4.1. Brief description
      • 4.4.2. Install MindOpt Python from the complete installation package
      • 4.4.3. pip install MindOpt Python package
      • 4.4.4. Python compilation example: mdo_mps
    • 4.5. Using Java
  • 5. Modeling and Optimization
    • 5.1. Linear Programming (LP)
      • 5.1.1. Modeling for linear programming
      • 5.1.2. Modeling and optimization in C
      • 5.1.3. Modeling and optimization in C++
      • 5.1.4. Modeling and optimization in Python
    • 5.2. Mixed Integer Linear Programming (MILP)
      • 5.2.1. 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
    • 5.3. 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. Semidefinite Programming (SDP)
      • 5.4.1. Semidefinite Programming Modeling
      • 5.4.2. Examples of SDP Problems
      • 5.4.3. SDP modeling and optimization in C language
      • 5.4.4. SDP modeling and optimization in C++ language
      • 5.4.5. SDP modeling and optimization in Python language
    • 5.5. Analysis of constraint conflicts
      • 5.5.1. C API: Mdo_computeIIS
      • 5.5.2. C++ API:computeIIS
      • 5.5.3. Python API: compute_iis
  • 6. Use Modeling Languages
    • 6.1. Modeling and optimization in AMPL
      • 6.1.1. Verify mindoptampl
      • 6.1.2. Install AMPL
      • 6.1.3. Parameters and return values of AMPL API
      • 6.1.4. Example of calling MindOpt by AMPL
    • 6.2. Modeling and optimization by Pyomo
      • 6.2.1. Install Pyomo
      • 6.2.2. Call the Pyomo API document
      • 6.2.3. Modeling example: mdo_pyomo_lo_ex1
    • 6.3. Modeling and optimization in PuLP
      • 6.3.1. Install PuLP
      • 6.3.2. Call the PuLP API
      • 6.3.3. Modeling example: mdo_pulp_lo_ex1
    • 6.4. MindOpt APL
  • 7. Remote computing service
    • 7.1. C/S architecture
    • 7.2. Deployment and installation
      • 7.2.1. Download and install the client SDK
      • 7.2.2. Deploy the compute server
      • 7.2.3. Web verification
    • 7.3. Operation and Maintenance
      • 7.3.1. Restart the compute server
      • 7.3.2. Clear disk data
    • 7.4. C/S API calls
    • 7.5. Example: C program
      • 7.5.1. Upload a model
      • 7.5.2. Obtain the result
    • 7.6. Example: C++ program
      • 7.6.1. Upload a model
      • 7.6.2. Obtain the result
    • 7.7. Example: Python program
      • 7.7.1. Upload a model
      • 7.7.2. Obtain the result
    • 7.8. Modeling in a client program
    • 7.9. Operations on the web page of the server
      • 7.9.1. Access and logon
      • 7.9.2. Workbench
      • 7.9.3. Job list
      • 7.9.4. Downloads list
  • 8. API Reference
    • 8.1. C API
      • 8.1.1. Environment management
      • 8.1.2. Model management
      • 8.1.3. IO management
      • 8.1.4. Attribute management
      • 8.1.5. Parameter management
      • 8.1.6. Solution management
    • 8.2. C++ API
      • 8.2.1. MdoCol
      • 8.2.2. MdoCons
      • 8.2.3. MdoEnv
      • 8.2.4. MdoException
      • 8.2.5. MdoExpr
      • 8.2.6. MdoExprLinear
      • 8.2.7. MdoModel
      • 8.2.8. MdoVar
    • 8.3. Python API
      • 8.3.1. MdoCol
      • 8.3.2. MdoCons
      • 8.3.3. MdoEnv
      • 8.3.4. MdoException
      • 8.3.5. MdoExpr
      • 8.3.6. MdoExprLinear
      • 8.3.7. MdoModel
      • 8.3.8. MdoVar
      • 8.3.9. quicksum
    • 8.4. Java API
    • 8.5. Definitions
      • 8.5.1. Data types in C/C++
      • 8.5.2. Result Code
      • 8.5.3. Solution code
      • 8.5.4. Alias of Attributes
      • 8.5.5. Constants
    • 8.6. Optional input parameters
      • 8.6.1. Integer parameters
      • 8.6.2. Real-number parameters
      • 8.6.3. String parameters
    • 8.7. Attributes during modeling and solving
      • 8.7.1. Model attributes
      • 8.7.2. Solution attributes
      • 8.7.3. Simplex method attributes
      • 8.7.4. IPM attributes
  • 9. Contact us
MindOpt
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  • 6. Use Modeling Languages

6. Use Modeling Languages¶

In this chapter, users will learn how to use modeling language to model problems and use MindOpt to solve them.

  • 6.1. Modeling and optimization in AMPL
    • 6.1.1. Verify mindoptampl
    • 6.1.2. Install AMPL
    • 6.1.3. Parameters and return values of AMPL API
    • 6.1.4. Example of calling MindOpt by AMPL
  • 6.2. Modeling and optimization by Pyomo
    • 6.2.1. Install Pyomo
    • 6.2.2. Call the Pyomo API document
    • 6.2.3. Modeling example: mdo_pyomo_lo_ex1
  • 6.3. Modeling and optimization in PuLP
    • 6.3.1. Install PuLP
    • 6.3.2. Call the PuLP API
    • 6.3.3. Modeling example: mdo_pulp_lo_ex1
  • 6.4. MindOpt APL
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