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)
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model

This repository contains the similarity metrics designed and evaluated in the paper, and instructions and code to re-run the experiments. Implementation in the deep-learning framework PyTorch

Steffen 86 Dec 27, 2022
Code for Deep Single-image Portrait Image Relighting

Deep Single-Image Portrait Relighting [Project Page] Hao Zhou, Sunil Hadap, Kalyan Sunkavalli, David W. Jacobs. In ICCV, 2019 Overview Test script for

438 Jan 05, 2023
The repo of the preprinting paper "Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection"

Inferring Spatial Uncertainty in Object Detection A teaser version of the code for the paper Labels Are Not Perfect: Inferring Spatial Uncertainty in

ZINING WANG 21 Mar 03, 2022
Doing the asl sign language classification on static images using graph neural networks.

SignLangGNN When GNNs 💜 MediaPipe. This is a starter project where I tried to implement some traditional image classification problem i.e. the ASL si

10 Nov 09, 2022
Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

CoMatch: Semi-supervised Learning with Contrastive Graph Regularization (Salesforce Research) This is a PyTorch implementation of the CoMatch paper [B

Salesforce 107 Dec 14, 2022
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution

DRSAN A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution Karam Park, Jae Woong Soh, and Nam Ik Cho Environments U

4 May 10, 2022
Code repository for "Free View Synthesis", ECCV 2020.

Free View Synthesis Code repository for "Free View Synthesis", ECCV 2020. Setup Install the following Python packages in your Python environment - num

Intelligent Systems Lab Org 253 Dec 07, 2022
Alternatives to Deep Neural Networks for Function Approximations in Finance

Alternatives to Deep Neural Networks for Function Approximations in Finance Code companion repo Overview This is a repository of Python code to go wit

15 Dec 17, 2022
ICLR21 Tent: Fully Test-Time Adaptation by Entropy Minimization

⛺️ Tent: Fully Test-Time Adaptation by Entropy Minimization This is the official project repository for Tent: Fully-Test Time Adaptation by Entropy Mi

Dequan Wang 204 Dec 25, 2022
A stable algorithm for GAN training

DRAGAN (Deep Regret Analytic Generative Adversarial Networks) Link to our paper - https://arxiv.org/abs/1705.07215 Pytorch implementation (thanks!) -

195 Oct 10, 2022
DeepOBS: A Deep Learning Optimizer Benchmark Suite

DeepOBS - A Deep Learning Optimizer Benchmark Suite DeepOBS is a benchmarking suite that drastically simplifies, automates and improves the evaluation

Aaron Bahde 7 May 12, 2020
DAN: Unfolding the Alternating Optimization for Blind Super Resolution

DAN-Basd-on-Openmmlab DAN: Unfolding the Alternating Optimization for Blind Super Resolution We reproduce DAN via mmediting based on open-sourced code

AlexZou 72 Dec 13, 2022
FEDn is an open-source, modular and ML-framework agnostic framework for Federated Machine Learning

FEDn is an open-source, modular and ML-framework agnostic framework for Federated Machine Learning (FedML) developed and maintained by Scaleout Systems. FEDn enables highly scalable cross-silo and cr

Scaleout 75 Nov 09, 2022
Unconstrained Text Detection with Box Supervisionand Dynamic Self-Training

SelfText Beyond Polygon: Unconstrained Text Detection with Box Supervisionand Dynamic Self-Training Introduction This is a PyTorch implementation of "

weijiawu 34 Nov 09, 2022
This is an open source python repository for various python tests

Welcome to Py-tests This is an open source python repository for various python tests. This is in response to the hacktoberfest2021 challenge. It is a

Yada Martins Tisan 3 Oct 31, 2021
ColossalAI-Benchmark - Performance benchmarking with ColossalAI

Benchmark for Tuning Accuracy and Efficiency Overview The benchmark includes our

HPC-AI Tech 31 Oct 07, 2022
Code for testing various M1 Chip benchmarks with TensorFlow.

M1, M1 Pro, M1 Max Machine Learning Speed Test Comparison This repo contains some sample code to benchmark the new M1 MacBooks (M1 Pro and M1 Max) aga

Daniel Bourke 348 Jan 04, 2023
Semantic segmentation models, datasets and losses implemented in PyTorch.

Semantic Segmentation in PyTorch Semantic Segmentation in PyTorch Requirements Main Features Models Datasets Losses Learning rate schedulers Data augm

Yassine 1.3k Jan 07, 2023
Cockpit is a visual and statistical debugger specifically designed for deep learning.

Cockpit: A Practical Debugging Tool for Training Deep Neural Networks

Felix Dangel 421 Dec 29, 2022
Using deep actor-critic model to learn best strategies in pair trading

Deep-Reinforcement-Learning-in-Stock-Trading Using deep actor-critic model to learn best strategies in pair trading Abstract Partially observed Markov

281 Dec 09, 2022