Optimization-Python

A collection of classic optimization problems solved using Python.

This project demonstrates how to formulate and solve optimization problems using libraries like PuLP, SciPy, and matplotlib for visualizations.

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Problems Covered

1. Linear Programming Problem (LPP)

• Solved using PuLP
• Supports constraints: ≤, ≥, =
• Maximization & Minimization problems
• See the code

2. Graphical Method for LPP (2D only)

• Plotted feasible region and optimal solution
• Uses matplotlib
• See the code
• 

3. Transportation Problem

• Formulated as LPP and solved using PuLP
• Handles supply and demand balance/unbalance cases
• See the code

4. Assignment Problem

• Solved using:
  • Hungarian Method (via scipy.optimize.linear_sum_assignment)
  • LPP formulation (via PuLP)
• See the code

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How to Run?

Follow these steps to set up and run the project on your local machine.

• Clone the repo

git clone https://github.com/sushruta19/Optimization-Python.git
cd Optimization-Python

• Create Virtual Env

python -m venv venv

• Activate Virtual Env

venv\Scripts\activate           # On Windows powershell
venv\Scripts\activate.bat       # On Windows CMD
source venv/bin/activate        # On linux bash

• Install requirements

pip install -r requirements.txt

• Run Jupyter Lab or Notebook

jupyter-lab                     # if you have installed this
jupyter-notebook