PPOPT

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

Installation

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]

In Python 3.11 and beyond there is currently an error with the quadprog package. An alternate version that fixed this error can be installed here.

pip install git+https://github.com/HKaras/quadprog/

Completed Features

Solver interface for mpLPs and mpQPs with the following algorithms
1. Serial and Parallel Combinatorial Algorithms
2. Serial and Parallel Geometrical Algorithms
3. Serial and Parallel Graph based Algorithms
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

Key Applications

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.

Quick Overview

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.

Citation

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}
}

Metadata

Release files for ppopt 1.6.12

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for ppopt 1.6.12
File	Interpreter	ABI	Platform	Reset
ppopt-1.6.12-py3-none-any.whl 96.6 kB Oct 21, 2024	Python 3	none	any	Details

Release files / ppopt-1.6.12-py3-none-any.whl

Download URL	ppopt-1.6.12-py3-none-any.whl
Size	96.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`30d3feba883e81dd89b60f497335a291651cf867057b47ac446fe237777b2160`
BLAKE2b-256 checksum How to use checksums	`e80260a3633e10d72a3f5c81689d46b30f2513fb5c503a2dab1b73dcb9649852`
Upload date	Oct 21, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.0.0 CPython/3.9.18`