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
  • »
  • 5. Modeling and Optimization »
  • 5.1. Linear Programming (LP)

5.1. Linear Programming (LP)¶

In this chapter, users will learn how to use MindOpt to solve linear Programming (LP) problems.

Note

For calling MindOpt using java, please refer to the examples in Java . Details about Java APIs will not be listed separately in this chapter.

  • 5.1.1. Modeling for linear programming
    • 5.1.1.1. Examples of linear programming problems
  • 5.1.2. Modeling and optimization in C
    • 5.1.2.1. Input by row: MdoLoEx1
    • 5.1.2.2. Input by column: MdoLoEx2
    • 5.1.2.3. Advanced example: MdoLoEx3
  • 5.1.3. Modeling and optimization in C++
    • 5.1.3.1. Input by row: MdoLoEx1
    • 5.1.3.2. Input by column: MdoLoEx2
    • 5.1.3.3. Advanced example: MdoLoEx3
  • 5.1.4. Modeling and optimization in Python
    • 5.1.4.1. Input by row: mdo_lo_ex1
    • 5.1.4.2. Input by column: mdo_lo_ex2
    • 5.1.4.3. Advanced example: mdo_lo_ex3
Next Previous

© Copyright 2023, Alibaba Cloud. Last updated on 2023 Aug 09.

Built with Sphinx using a theme provided by Read the Docs.