Continuous Piecewise Linear (CPWL) Fitting

This project provides a robust Python environment for fitting optimal Continuous Piecewise Linear (CPWL) functions to datasets in general dimensions ($n$D).

The core script formulates the fitting problem as a Mixed-Integer Linear Programming (MILP) model, allowing for precise optimization using Google's OR-Tools. It supports a variety of industry-standard solvers, including GUROBI, HiGHS, and SCIP.

Additionally, the environment includes built-in tools to visualize the resulting CPWL functions for 2D and 3D datasets.

Prerequisites

• Python 3.10 or higher
• Git

Installation

1. Clone the repository:

   git clone [https://github.com/quentinplsrd/cpwl-nd-optimization.git](https://github.com/quentinplsrd/cpwl-nd-optimization.git)
   cd cpwl-nd-optimization

2. Create a virtual environment:

   • Windows:

     python -m venv .venv

   • Mac/Linux:

     python3 -m venv .venv

3. Activate the environment:

   • Windows: .venv\Scripts\activate
   • Mac/Linux: source .venv/bin/activate

4. Install dependencies:

   pip install -r requirements.txt

Usage

To run the main analysis script:

python src/main_script.py