This folder contains a Jupyter Notebook demonstrating how to integrate NVIDIA cuOpt as a solver backend for optimization problems modeled with Pyomo.

Pyomo is a Python-based open-source software package that supports a diverse set of optimization capabilities for formulating, solving, and analyzing optimization models.

Pyomo supports cuOpt as a backend solver, allowing you to leverage GPU-accelerated optimization while using Pyomo's intuitive modeling syntax. This integration provides:

• Familiar API: Use Pyomo's pythonic syntax for modeling
• GPU Acceleration: Benefit from cuOpt's high-performance GPU-based solving
• Easy Solver Switching: Compare different solvers by simply changing the solver parameter

This notebook demonstrates the classic p-median problem:

• Problem: Choosing facility locations to minimize the weighted distance while meeting assignment constraints.
• Approach: Model the problem using Pyomo and solve with cuOpt
• Features:
  • Setting up decision variables and constraints with Pyomo
  • Solving with setting solver = pyo.SolverFactory("cuopt") parameter
  • Analyzing and visualizing results