SCIPDex

Currently still WIP, feedback and contributions are very welcome!

A collection of interactive exercises to get you started (and more) with PySCIPOpt. It is designed to be used along with the PySCIPOpt documentation.

The exercises are split into folders by topic. Each folder contains a README.md file describing the exercises and python files with TODOs. Each folder has test files that checks if the exercises are correctly solved.

You can also find a Glossary containing light explanations of the terms and acronyms used in the exercises.

Installation

The exercises depend only on PySCIPOpt (and its dependencies) and pytest. You can install them with:

pip install -r requirements.txt

For other ways of installing PySCIPOPt, please refer to the PySCIPOpt documentation.

Getting Started

Now you're ready to start the exercises 🚀 The repository is divided into chapters, each containing multiple problems. In each chapter there is a README describing each of the problems, and in each of the problems there is a README describing the problem in detail and the exercises to be completed. Besides this README, the problem folder also has a [problem].py file to solve the exercises and a test_[problem].py file for testing your solution. More complex problems might need more files to structure the code better, but the corresponding README's explain the structure. Here is a brief description of each chapter:

• intro: An introduction to PySCIPOpt and its more elementary functionalities.
• modeling:
• event_handler: Unlocking the ability to interrupt the solving process when a predetermined event is caught, and executing user-code.
• primal_heuristic
• separator
• branching_rule
• branch_and_price

We recommend starting by going to intro and following the instructions in the README.md file.

Checking your solutions

Each exercise set has a bunch of test files, which you can run by

pytest EXCERCISE_FOLDER

for instance, to run all tests in the modeling chapter, you can run

pytest modeling

You can also run specific test files, for example, to run the tests for the knapsack problem set in the modeling chapter, you can run

pytest modeling/knapsack/test_knapsack.py

Contributing

If you find any issues or have suggestions for improvements, please open an issue or a pull request. Thank you!