Software Platform for solving and manipulating multiparametric programs in Python

Overview

PPOPT

Python package Documentation Status PyPI

Python Parametric OPtimization Toolbox (PPOPT) is a software platform for solving and manipulating multiparametric programs in Python. This package is still in development but the following features are complete and are in full working order.

Installation

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

pip install -e git+https://github.com/mmihaltz/pysettrie.git#egg=pysettrie
pip install ppopt

Quick Overview

To give a fast primer of what we are doing, we are solving multiparametric programming problems (fast) by writting parallel algorithms efficently. Here is a quick sclaing analysis on a large multiparametric program.

image image

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.

image

Completed Features

  • Solver interface for mpLPs and mpQP with the following algorithms
    1. Serial and Parallel Combinatorial Algorithm
    2. Serial and Parallel Geometrical Algorithm
    3. Serial and Parallel Graph based Algorithm
  • Multiparametric solution export to C++, Javacript, Matlab, 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.

You might also like...
A module for solving and visualizing Schrödinger equation.
A module for solving and visualizing Schrödinger equation.

qmsolve This is an attempt at making a solid, easy to use solver, capable of solving and visualize the Schrödinger equation for multiple particles, an

Deep learning library for solving differential equations and more

DeepXDE Voting on whether we should have a Slack channel for discussion. DeepXDE is a library for scientific machine learning. Use DeepXDE if you need

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

JAX: Autograd and XLA Quickstart | Transformations | Install guide | Neural net libraries | Change logs | Reference docs | Code search News: JAX tops

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

JAX: Autograd and XLA Quickstart | Transformations | Install guide | Neural net libraries | Change logs | Reference docs | Code search News: JAX tops

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

Some simple programs built in Python: webcam with cv2 that detects eyes and face, with grayscale filter
Some simple programs built in Python: webcam with cv2 that detects eyes and face, with grayscale filter

Programas en Python Algunos programas simples creados en Python: 📹 Webcam con c

Code for Graph-to-Tree Learning for Solving Math Word Problems (ACL 2020)

Graph-to-Tree Learning for Solving Math Word Problems PyTorch implementation of Graph based Math Word Problem solver described in our ACL 2020 paper G

 Exploration-Exploitation Dilemma Solving Methods
Exploration-Exploitation Dilemma Solving Methods

Exploration-Exploitation Dilemma Solving Methods Medium article for this repo - HERE In ths repo I implemented two techniques for tackling mentioned t

Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary Differential Equations

ODE GAN (Prototype) in PyTorch Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary

Comments
  • Bitset debug

    Bitset debug

    I've been trying to use autogenerated c++ code to do control allocation on an aircraft. I've discovered that the original code finds incorrect critical regions. Root cause is that bitsets order bits from right to left, but code referenced bitsets from left to right.

    opened by AeroTH310 1
  • Control allocation example

    Control allocation example

    I've out together a basic octocopter example in a .rst file in a style similar to the existing tutorial. I've attempted to get it to display properly on a Read the Docs page, but have not yet been successful. Anyway, I felt it shouldn't delay the PR.

    opened by AeroTH310 0
  • Adds the mixed integer problem type and export code

    Adds the mixed integer problem type and export code

    1. Added enumeration algorithm for the mixed-integer case of mpMILP and mpMIQP
    2. Fixed plotting export file name not to include a timestamp
    3. Removed output on constraint processing
    opened by DKenefake 0
  • No module named 'settrie' when calling the method of solve_mpqp

    No module named 'settrie' when calling the method of solve_mpqp

    In the source code of ppopt.mp_solvers.solve_mpqp, there is From settrie import SetTrie at the top, but there is no such a package in the network, surly 'pip install' fails to work.

    I know the author want to create a trie, but there is a package missing. Pls fix this bug, thanks a lot!

    opened by TimberJ99 1
Releases(Release)
  • Release(Sep 25, 2021)

    This is the initial public release. Please feel free to use this to solve your parametric programming problems.

    If you run into any errors or bugs, please feel free to let us know!

    Source code(tar.gz)
    Source code(zip)
Official implementation for the paper "Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection"

Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection PyTorch code release of the paper "Attentive Prototypes for Sour

Deepti Hegde 23 Oct 17, 2022
Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight

Second-order Neural ODE Optimizer (NeurIPS 2021 Spotlight) [arXiv] ✔️ faster convergence in wall-clock time | ✔️ O(1) memory cost | ✔️ better test-tim

Guan-Horng Liu 39 Oct 22, 2022
This is the repository for our paper SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking

SimpleTrack This is the repository for our paper SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking. We are still working on writing t

TuSimple 189 Dec 26, 2022
A large-scale face dataset for face parsing, recognition, generation and editing.

CelebAMask-HQ [Paper] [Demo] CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA da

switchnorm 1.7k Dec 26, 2022
Synthetic Scene Text from 3D Engines

Introduction UnrealText is a project that synthesizes scene text images using 3D graphics engine. This repository accompanies our paper: UnrealText: S

Shangbang Long 215 Dec 29, 2022
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation

AtlasNet [Project Page] [Paper] [Talk] AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation Thibault Groueix, Matthew Fisher, Vladimir

577 Dec 17, 2022
All course materials for the Zero to Mastery Machine Learning and Data Science course.

Zero to Mastery Machine Learning Welcome! This repository contains all of the code, notebooks, images and other materials related to the Zero to Maste

Daniel Bourke 1.6k Jan 08, 2023
Unofficial Implementation of MLP-Mixer, Image Classification Model

MLP-Mixer Unoffical Implementation of MLP-Mixer, easy to use with terminal. Train and test easly. https://arxiv.org/abs/2105.01601 MLP-Mixer is an arc

Oğuzhan Ercan 6 Dec 05, 2022
DiffQ performs differentiable quantization using pseudo quantization noise. It can automatically tune the number of bits used per weight or group of weights, in order to achieve a given trade-off between model size and accuracy.

Differentiable Model Compression via Pseudo Quantization Noise DiffQ performs differentiable quantization using pseudo quantization noise. It can auto

Facebook Research 145 Dec 30, 2022
Bridging Composite and Real: Towards End-to-end Deep Image Matting

Bridging Composite and Real: Towards End-to-end Deep Image Matting Please note that the official repository of the paper Bridging Composite and Real:

Jizhizi_Li 30 Oct 31, 2022
Paddle implementation for "Highly Efficient Knowledge Graph Embedding Learning with Closed-Form Orthogonal Procrustes Analysis" (NAACL 2021)

ProcrustEs-KGE Paddle implementation for Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis 🙈 A more detailed re

Lincedo Lab 4 Jun 09, 2021
Xintao 1.4k Dec 25, 2022
Learning nonlinear operators via DeepONet

DeepONet: Learning nonlinear operators The source code for the paper Learning nonlinear operators via DeepONet based on the universal approximation th

Lu Lu 239 Jan 02, 2023
A working implementation of the Categorical DQN (Distributional RL).

Categorical DQN. Implementation of the Categorical DQN as described in A distributional Perspective on Reinforcement Learning. Thanks to @tudor-berari

Florin Gogianu 98 Sep 20, 2022
Deep GPs built on top of TensorFlow/Keras and GPflow

GPflux Documentation | Tutorials | API reference | Slack What does GPflux do? GPflux is a toolbox dedicated to Deep Gaussian processes (DGP), the hier

Secondmind Labs 107 Nov 02, 2022
Code release to accompany paper "Geometry-Aware Gradient Algorithms for Neural Architecture Search."

Geometry-Aware Gradient Algorithms for Neural Architecture Search This repository contains the code required to run the experiments for the DARTS sear

18 May 27, 2022
Code release for Hu et al. Segmentation from Natural Language Expressions. in ECCV, 2016

Segmentation from Natural Language Expressions This repository contains the code for the following paper: R. Hu, M. Rohrbach, T. Darrell, Segmentation

Ronghang Hu 88 May 24, 2022
Original Implementation of Prompt Tuning from Lester, et al, 2021

Prompt Tuning This is the code to reproduce the experiments from the EMNLP 2021 paper "The Power of Scale for Parameter-Efficient Prompt Tuning" (Lest

Google Research 282 Dec 28, 2022
PyTorch implementations of deep reinforcement learning algorithms and environments

Deep Reinforcement Learning Algorithms with PyTorch This repository contains PyTorch implementations of deep reinforcement learning algorithms and env

Petros Christodoulou 4.7k Jan 04, 2023