Rubi: Symbolic integrator based on an extensive system of integration rules
Rubi is an open-source symbolic integrator using over 6,700 rules, outperforming Mathematica and Maple. It aids learning by providing antiderivatives and steps, with source code available on GitHub.
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Rubi is a symbolic integrator that utilizes a comprehensive collection of over 6,700 integration rules organized in a decision tree format. This system is designed to find optimal antiderivatives for a wide range of mathematical expressions while also providing the rules and intermediate steps used in the integration process, making it a valuable educational tool. Users can install Rubi if they have the Mathematica computer algebra system, and they can access a test suite of over 72,000 integration problems to evaluate Rubi's performance against other symbolic integrators like Mathematica and Maple. Rubi has demonstrated superior performance in these tests, with results graded based on their efficiency and complexity. The system is open-source, and its source code is available on GitHub, where users can also report issues or suggest new features. Additionally, Rubi has been referenced in various academic articles, highlighting its significance in the field of symbolic integration.
- Rubi employs over 6,700 integration rules organized in a decision tree format.
- It provides both the optimal antiderivative and the steps taken to achieve it, aiding in learning.
- Rubi outperforms other symbolic integrators like Mathematica and Maple in extensive testing.
- The system is open-source, with its source code available on GitHub for public access.
- Users can report bugs and suggest features through GitHub and join discussions on Gitter.
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8 commentsAFAIK, this is the only recent benchmark-type study that compares modern CAS software (Mathematica vs Maxima vs FriCAS vs etc).
Unfortunately the answer was in terms of hypergeometric functions and was scarcely more useful than the unevaluated integral. In any case I found Rubi to be very impressivein terms of results.
Digging into the code wasn't much fun, as it's just a colossal Mathematica rules engine, like reading Gradshteyn and Ryzhik, but with square brakets everywhere.
https://12000.org/my_notes/kamek/kamke_differential_equation...
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