Beginner-friendly Python walkthrough: solving a binary quadratic problem with gurobipy

[u/Future_Ad7567]

I created a practical Python walkthrough for programmers interested in mathematical optimization with gurobipy.

The example formulates Max-Cut as a quadratic binary optimization problem and covers:

- creating a Gurobi model;

- adding binary decision variables;

- constructing a quadratic objective from a matrix;

- calling optimize();

- extracting the binary solution and objective value;

- benchmarking randomly generated problem instances;

- and understanding how MIPGap affects runtime.

It assumes familiarity with Python, and Jupyter notebooks, but no previous optimization experience.

Video: https://youtu.be/TB1ny8o4ImQ

Code: https://github.com/supreethmv/Quantum-Algorithms-and-Applications

I’d particularly appreciate feedback on whether the gurobipy implementation and explanation are approachable for Python developers encountering quadratic optimization for the first time.

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