2.1. Quick Start¶
MindOpt is a high-performance optimization solver designed to address a wide range of large-scale mathematical optimization problems. It integrates multiple advanced algorithms tailored for various optimization models, enabling users to efficiently find optimal solutions for complex business challenges. In this chapter, we will briefly introduce MindOpt’s core features and guide you on how to start using it.
2.1.1. Overview of Solving Capabilities¶
MindOpt supports the following optimization problems types.
To enhance modeling efficiency, MindOpt offers advanced features such as:
Irreduciable Infeasible Subsystem (IIS): Helps users identify key constraints that lead to feasibility conflicts when a model is infeasible;
Callback: Helps users practice personalized heuristic solving strategies to optimize solving speed.
Additional problem types and features are under development, and users are encouraged to stay tuned for updates.
2.1.2. Acquisition and Deployment¶
MindOpt offers multiple acquisition methods to cater to different user requirements:
Download and Installation: the latest version of MindOpt can be obtained from the Download and install the latest Optimization Solver SDK.
Python Users: MindOpt can be installed via pip using the command
pip install mindoptpy
.Cloud-Based Development Platform: MindOpt provides a cloud-based development environment with computing resources and an integrated IDE, along with Sample Cases to help users quickly get started.
Enterprise Users: High-performance versions of MindOpt are available for enterprise users. Contact Us for more information.
MindOpt supports different deployment methods to meet different user environments:
Local PC
High-performance server
Computing cluster
Cloud computing resources
MindOpt also supports high-concurrency solving and parallel processing on computing clusters. Additionally, a cluster version is available to meet large-scale needs.
Before acquisition, please note that MindOpt supports the following operating systems:
Operating systems |
Requirements |
---|---|
Windows |
Windows 10 or higher |
Linux |
GLIBC 2.17 or higher |
macOS |
10.9 or higher for x64, 12.0 or higher for arm64 |
After installation, users need to obtain the license and complete the configuration process. Please refer to: License Setup.
2.1.3. Usage and Modeling Tools¶
Users can call the solver or write their optimization programs through the command line or APIs in the following languages:
When using APIs, please ensure that the versions are compatible with the requirements of the supported languages.
Languages |
Recommended compilers |
---|---|
C |
Visual Studio 2019 or higher, GCC 6.5 or higher, Clang 13.0 or higher |
C++ |
Visual Studio 2019 or higher, G++ 6.5 or higher, Clang++ 13.0 or higher |
C# |
.NET SDK 8.0 or higher |
Python |
Python 3.8 or higher |
Java |
JDK 1.8 or higher |
MATLAB |
MATLAB R2021b or higher |
MindOpt provides flexible modeling options to suit diverse user needs: MindOpt APIs, model data, and modeling languages.
APIs: MindOpt offers comprehensive APIs in multiple languages (C, C++, Python, Java, MATLAB) to facilitate seamless integration and customization. For more details, please refer to API.
Standard Model Data Formats: MindOpt supports direct reading of optimization problems in the following formats:
.mps
format
.lp
format
.dat-s
format
.nl
format
Modeling Languages: MindOpt is compatible with several popular modeling languages, including MindOpt APL (a proprietary language developed by MindOpt), AMPL, Pyomo, PuLP, and JuMP.
2.1.4. Case Examples and Learning Tools¶
MindOpt offers extensive documentation and application examples for users:
The basic use of MindOpt’s API for formulating and solving models is covered in Modeling and Optimization and API.
On the MindOpt Studio Platform, we offer a wider range of application examples.