Minimaxer: Optimization & Decision-Making Platform

Minimaxer is a comprehensive platform for mathematical optimization and multi-criteria decision-making. It features a robust FastAPI backend and an interactive Gradio web interface.

🚀 Features

1. Linear Programming (LP & ILP)

• Solver: Powered by PuLP and the CBC solver.
• Capabilities: Supports continuous, integer, and binary variables.
• Interactive Modeling: Build, preview, and solve optimization problems in real-time.

2. Multi-Criteria Decision Making (MCDM)

• TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution. Supports hybrid criteria (Quantitative, Qualitative, Ranking).
• ELECTRE I: Elimination and Choice Expressing Reality. Uses concordance and discordance indices to find the set of best alternatives (Kernel).

3. Modern Web Interface

• Built with Gradio for a seamless user experience.
• Dynamic alternative management and criteria configuration.
• Mathematical model preview for verification.

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🛠️ Installation & Setup

Prerequisites

• Python 3.10+
• Docker & Docker Compose (optional)

Local Development

1. Install Dependencies:

   pip install -r server/requirements.txt
   pip install -r client/requirements.txt

2. Run the Server:

   # From the project root
   uvicorn server.app.main:app --reload

   API will be available at http://localhost:8000. Docs at /docs.

3. Run the Client:

   python client/app.py

   Web interface will be available at http://localhost:7860.

Running with Docker

Use the provided utility scripts:

./start.sh   # Build and start containers
./logs.sh    # View logs
./stop.sh    # Stop and remove containers

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📖 API Usage

The backend exposes several endpoints for different solving methods under the /api/solvers/ prefix.

Method	Endpoint	Description
Linear	POST /api/solvers/linear	LP/ILP solving via PuLP
TOPSIS	POST /api/solvers/topsis	Multi-criteria ranking
ELECTRE	POST /api/solvers/electre	Outranking method (ELECTRE I)

Example: Linear Solver

Request:

{
  "name": "Production_Optimization",
  "direction": "maximize",
  "variables": [
    {"name": "x", "low_bound": 0, "category": "Integer"}
  ],
  "objective": {"coefficients": {"x": 10}},
  "constraints": [
    {"coefficients": {"x": 2}, "operator": "<=", "rhs": 100}
  ]
}

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🧪 Testing

We use pytest for unit and integration testing.

# Run all tests
pytest tests/ -v

# Run backend specific tests
pytest server/tests/ -v

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📁 Project Structure

├── client/          # Gradio Web Interface
│   ├── ui/          # Layout and dynamic UI components
│   └── core/        # UI logic and model builders
├── server/          # FastAPI Backend
│   └── app/         # Core application logic
│       ├── api/     # REST endpoints (Routers)
│       └── services/# Solving logic (PuLP, NumPy)
├── shared/          # Shared Pydantic schemas
└── notebooks/       # Research & prototyping

📄 License

This project is licensed under the MIT License.