This project explores pricing optimization using convex and non-convex optimization techniques. The main goal is to analyze pricing strategies, evaluate performance differences, and demonstrate how different optimization approaches impact revenue outcomes.
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├── data/ # Raw and processed datasets
├── notebooks/ # Jupyter notebooks for experiments and analysis
├── report/ # Project report and findings
├── requirements.txt # Python dependencies
└── README.md # Project documentation
- Convex Optimization: Solves pricing problems with guaranteed global optimality using
cvxpy. - Non-Convex Optimization: Explores complex revenue landscapes using techniques like differential evolution.
- Visualization: Plots comparing convex vs non-convex results and revenue curves.
- Modular Notebooks: Step-by-step analysis that is easy to understand and extend.
- Python ≥ 3.8
- Packages:
numpy,pandas,matplotlib,seaborn,cvxpy,scipy
Install dependencies with:
pip install -r requirements.txt- Clone the repository:
git clone https://github.com/Salmaazoz22/convex-optimization-project.git
cd convex-optimization-project- Explore notebooks in the
notebooks/folder for step-by-step analysis. - Check the
report/folder for the full project report. - Load datasets from the
data/folder.
- Convex Optimization: Efficiently finds global optimum prices.
- Non-Convex Optimization: Can explore better revenue points in complex landscapes but may require more computation.
- Comparative analysis highlights trade-offs between speed, accuracy, and solution complexity.
By:
- Salma Mohammed Abd-ElAziz
- Ahmed Alaa Abd-ElReheem
- Rawan Essam-ElDin Fahmy
- Mahmoud Hossam-ElDin Mahmoud
- Mohammed Saied Ahmed
- Nour Hatem Mohammed
- Yassin Yasser Zakaria
- CVXPY Documentation
- SciPy Optimize
- [Matplotlib & Seaborn for Visualization](https://matplotlib.org/, https://seaborn.pydata.org/)