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CS348-Applied-Optimization

This repository contains Python implementations of various optimization algorithms, including Gradient Descent and Newton's Method, developed as part of the CS348: Applied Optimization course.

📚 Overview

The course focuses on practical optimization techniques applicable in machine learning, data science, and engineering. This repository showcases implementations of foundational optimization methods, demonstrating their application and performance.

🧪 Key Implementations

  • Gradient Descent: An iterative method for finding the minimum of a function.
  • Newton's Method: A root-finding algorithm that uses function values and derivatives.

🚀 Getting Started

To run the scripts:

  1. Clone this repository:

    git clone https://github.com/ehrg1/CS348-Applied-Optimization.git
cd CS348-Applied-Optimization

  2. Install the required Python packages:

    pip install -r requirements.txt

  3. Execute the desired script:

    python gradient&newton.py

🧪 Usage

Each script is designed to be run independently. For detailed instructions and examples, refer to the comments within the respective Python files.