This project implements an interior point method for solving constrained optimization problems in Python, using only numpy for core optimization routines. It supports both inequality and equality constraints, and includes visualization tools for feasible regions and optimization paths.

• Log-barrier interior point method for constrained minimization
• Support for both 2D and 3D problems
• Visualization of feasible regions and central path
• Unit tests for quadratic and linear programming examples

• Python 3.8+
• numpy
• pandas
• matplotlib

1. Install dependencies:

   pip install -r requirements.txt

2. Run unit tests:

   python -m unittest discover tests

3. Use the InteriorPoint class in your own scripts for constrained optimization problems.

• src/ - Source code for optimization algorithms
• tests/ - Unit tests and example problems

See tests/test_constrained_min.py for example usage and test cases.