Inquiry about Backend Support and Access to Cut Planes in Python-MIP

[ZhongkuiMa]

Hi,

First of all, thank you for your amazing work on Python-MIP. It's a fantastic tool that unifies multiple solvers under a clean and intuitive API, which makes modeling and solving optimization problems in Python both accessible and powerful. The consistency it provides across solvers is a huge productivity boost, especially for researchers and developers working on optimization-driven applications.

I’m particularly impressed by how well Python-MIP abstracts over solvers like CBC and Gurobi. That said, I was wondering whether there are any plans to support additional solver backends in the future—such as CPLEX, GLPK, HiGHS, or other open solver interfaces. Expanding backend support could significantly increase flexibility for users in both academic and industrial settings, especially those with constraints on licensing or computing environments.

Additionally, I’d like to ask whether there is any possibility of exposing interfaces to extract cutting planes generated during the solving process. In some research areas—such as neural network verification, e.g., GCP-CROWN—access to cut information (e.g., Gomory, MIR, or user cuts) can be highly valuable. Being able to retrieve and analyze these cuts would enable hybrid optimization strategies, solver-informed heuristics, and deeper insights into problem structures.

I understand that not all solvers expose this information, and implementation may vary, but even partial support (when available) would be an exciting step forward for research and experimental applications. I’d really appreciate hearing your thoughts on whether this would be feasible or potentially on the roadmap.

Thanks again for developing and maintaining such a powerful and elegant tool!