ND-Pyomo-Cookbook | A repository of Pyomo examples | Machine Learning library

by jckantor Jupyter Notebook Version: Current License: MIT

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kandi X-RAY | ND-Pyomo-Cookbook Summary

ND-Pyomo-Cookbook is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning applications. ND-Pyomo-Cookbook has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

ND Pyomo Cookbook is a collection of notebooks showing how to use Pyomo to solve modeling and optimization problems. With Pyomo, one can embed within Python an optimization model consisting of decision variables, constraints, and an optimization objective. A rich set of features enables the modeling and analysis of complex systems. The notebooks in this collection were developed for instructional purposes at Notre Dame. Originally developed using the Anaconda distribution of Python, the notebooks have been updated to open directly Google Colaboratory where they can be run using only a browser window. PyomoFest at Notre Dame was held June 5-7, 2018. This repository contains the agenda, slides and exercises distributed during that event.

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ND-Pyomo-Cookbook has a low active ecosystem.

It has 284 star(s) with 122 fork(s). There are 17 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 13 have been closed. On average issues are closed in 492 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ND-Pyomo-Cookbook is current.

Quality

ND-Pyomo-Cookbook has 0 bugs and 0 code smells.

Security

ND-Pyomo-Cookbook has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ND-Pyomo-Cookbook code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ND-Pyomo-Cookbook is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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ND-Pyomo-Cookbook releases are not available. You will need to build from source code and install.

It has 615899 lines of code, 19 functions and 68 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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ND-Pyomo-Cookbook Key Features

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ND-Pyomo-Cookbook Examples and Code Snippets

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Community Discussions

Trending Discussions on ND-Pyomo-Cookbook

Pyomo accesing/retrieving dual variables - shadow price with binary variables

QUESTION

Pyomo accesing/retrieving dual variables - shadow price with binary variables

Asked 2021-Jan-01 at 16:25

I am pretty new to optimization in general and pyomo in particular, so I apologize in advance for any rookie mistakes.

I have defined a simple unit commitment exercise (example 3.1 from [1]) using [2] as starting point. I got the correct result and my code runs, but I have a few questions regarding how to access stuff.

...

ANSWER

Answered 2021-Jan-01 at 16:25

To answer 1, you can dynamically get the constraint objects from your model using model.component_objects(pyo.Constraint) which will return an iterator of your constraints, which keeps your from having to hard-code the constraint names. It gets tricky for indexed variables because you have to do an extra step to get the slacks for each index, not just the constraint object. For the duals, you can iterate over the keys attribute to retrieve those values.

Source https://stackoverflow.com/questions/65523319

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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Install ND-Pyomo-Cookbook

You can download it from GitHub.

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