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What is MINTO?

MINTO is a Python framework that supports the entire process of mathematical optimization experiments, from planning and execution to analysis of results.

Core Concepts

MINTO is designed around three key concepts:

  1. Reproducibility and Shareability
    Systematically records experimental settings, execution, and results to ensure reproducibility and facilitate knowledge sharing within teams.

  2. Comprehensive Experiment Support
    Provides comprehensive support for the complete experimental process, including planning, execution, and analysis.

  3. Standardized Benchmark Environment
    Offers standard optimization problems through the problems module to facilitate benchmark experiments, enabling efficient performance evaluation of new algorithms and methods.

What is MINTO used for?

MINTO supports mathematical optimization experiments in the following aspects:

  1. Experiment Planning and Design
  2. Provision of standard optimization problems
  3. Management of parameter settings

  4. Systematization of experimental conditions

  5. Experiment Execution

  6. Automation of solver configuration and execution
  7. Automatic recording of experimental processes

  8. Error handling and log management

  9. Result Analysis and Sharing

  10. Structured storage of experimental results
  11. Comparative analysis between different experimental conditions
  12. Sharing of experimental results among team members

Who uses it?

MINTO's target users include: - Researchers and engineers in mathematical optimization - Project members solving optimization problems in companies - Anyone conducting optimization experiments

MINTO supports experiments of all scales, from small to large-scale implementations.

Core Features

1. Experiment Process Support

  • Support for systematic experiment planning
  • Automation of experimental condition settings and management
  • Efficient execution and recording of experiments

2. Benchmark Environment

  • Standard problems through the problems module
  • Management of benchmark datasets
  • Standardization of performance evaluation

3. Data Management and Sharing

  • Systematic management of problem definitions, parameters, and results
  • Experiment sharing via GitHub and Docker
  • Facilitation of knowledge sharing among teams

Benefits

For Researchers and Developers

  • Easy access to standard benchmark environments
  • Ensured experimental reproducibility
  • Systematic analysis of experimental results

For Teams

  • Easy sharing of experimental insights
  • Establishment of standardized experimental processes
  • Efficient collaboration

MINTO enhances the efficiency of the entire mathematical optimization experimental process, enabling high-quality research and development. Through standardized problem sets and tools, it provides consistent support from experiment planning to result sharing.