Python-MIP (Mixed-Integer Programming) is a Python library for modeling and solving Mixed-Integer Linear Programs (MIPs). It provides a high-level, intuitive API inspired by Pulp, offering access to advanced solver features like cut generation, lazy constraints, and solution pools. The library integrates with popular open-source solvers like COIN-OR CBC and HiGHS, as well as the commercial Gurobi solver. It is actively maintained, with version 1.17.6 being the latest, and aims for high performance and extensibility, supporting Python 3.10+ and PyPy for faster model generation.
pip install mipVerified import paths — ran on the pinned version, not inferred.
This quickstart solves a 0/1 Knapsack Problem, demonstrating model creation, variable definition, objective function, adding constraints, optimization, and result querying.
Consider optimizing your model construction logic, especially for very large, dense problems. For extremely performance-critical setups, exploring alternative data structures for linear expressions might be necessary, or consider if another library is more suitable for problem generation speed.
Check for solver-specific logs if available. Ensure your model is well-formed, data types are correct, and variable bounds are consistent. Sometimes simplifying the problem or updating the `mip` package and its underlying solver binaries can resolve such issues. Contacting developers with a minimal reproducible example is also an option.
Upgrade to the latest version of `python-mip`. Version 1.13.0 and later include updated CBC binaries that addressed some longstanding bugs on MacOS and likely improved stability on Windows as well.
Ensure your Python environment is 3.10 or newer. Python 3.11 support was explicitly added in version 1.15.0.
Review the HiGHS integration documentation for 1.17.x to leverage the new, official support. `highsbox` is now automatically installed as an optional dependency.
Inspect the model for inconsistencies (e.g., conflicting constraints, incorrect bounds). Try a simpler version of the model to isolate the issue. Ensure `mip` and its solver dependencies are up to date. Check for solver-specific log files if enabled.
Refactor model construction to use iterative loops instead of recursion where possible. For expressions, leverage `mip.xsum()` for summations. If necessary, `sys.setrecursionlimit()` can be increased, but this is generally not recommended as a primary fix.
Profile your code to pinpoint exact bottlenecks. Ensure you're using efficient methods like `m.add_var_tensor()` for blocks of variables and `xsum()` for summations. Consider generating model data using NumPy/Pandas and then efficiently transferring it to the MIP model. For extremely large models, sometimes other modeling tools or direct solver APIs might offer faster model construction if that is the primary bottleneck.
Update `python-mip` to the latest version (`pip install --upgrade mip`). Version 1.13.0 and newer include updated CBC binaries which improved stability. If the problem persists, ensure your C++ redistributables are up to date.