Optimization Methods with Python

A Python implementation of various optimization methods for multidimensional functions using Streamlit with filled and line contour plot of optimization process. It's available online at Optimization

Implemented Methods

Explore a selection of implemented methods, including:

• Gradient Descent with constant, exact line search and backtracing step size
• Scaled Gradient Descent with Diagonal Reversed Hessian and Custom Matrix
• Newton's Method
• Damped Newton's Method
• Hybrid Gradient-Newton Method

Installation and Usage

1. Clone the repository:

   git clone https://github.com/mehdimhb/optimization.git

2. Change to the project directory:

   cd optimization

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run app.py

The application should now be accessible in your web browser at http://localhost:8501.

Contributing

Contributions are always welcome! If you have any ideas or suggestions, please feel free to open an issue or a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more information