Python Parametric OPtimization Toolbox (PPOPT) is a software platform for solving and manipulating multiparametric programs in Python.

Currently, PPOPT requires Python 3.7 or higher and can be installed with the following commands.

pip install ppopt

To install PPOPT and install all optional solvers the following installation is recommended.

pip install ppopt[optional]

• Solver interface for mpLPs and mpQPs with the following algorithms
  1. Serial and Parallel Combinatorial Algorithm
  2. Serial and Parallel Geometrical Algorithm
  3. Serial and Parallel Graph based Algorithm
• Solver interface for mpMILPs and mpMIQPs with the following algorithms
  1. Enumeration based algorithm
• Multiparametric solution export to C++, JavaScript, and Python
• Plotting utilities
• Presolver and Conditioning for Multiparametric Programs

• Explicit Model Predictive Control
• Multilevel Optimization
• Integrated Design, Control, and Scheduling
• Robust Optimization

For more information about Multiparametric programming and it's applications, this paper is a good jumping point.

To give a fast primer of what we are doing, we are solving multiparametric programming problems (fast) by writing parallel algorithms efficiently. Here is a quick scaling analysis on a large multiparametric program with the combinatorial algorithm.

Here is a benchmark against the state of the art multiparametric programming solvers. All tests run on the Terra Supercomputer at Texas A&M University. Matlab 2021b was used for solvers written in matlab and Python 3.8 was used for PPOPT.

Since a lot of time and effort has gone into PPOPT's development, please cite the following publication if you are using PPOPT for your own research.

@incollection{kenefake2022ppopt,
  title={PPOPT-Multiparametric Solver for Explicit MPC},
  author={Kenefake, Dustin and Pistikopoulos, Efstratios N},
  booktitle={Computer Aided Chemical Engineering},
  volume={51},
  pages={1273--1278},
  year={2022},
  publisher={Elsevier}
}