ICMS 2024 Session

Machine Learning within
Computer Algebra Systems

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Aim and Scope

Computer Algebra Systems (CASs) play an important role in mathematical research. CASs are immensely complicated pieces of software that allow the user to represent and manipulate mathematical objects within a computer. Handling these objects requires expensive computations: algorithms often contain choices that do not affect correctness, but can dramatically affect the resources required.

Heuristic methods try and choose the most appropriate option. Typically, these heuristics are designed by humans who study at most a few hundred examples. However, it has been shown that machine learning can outperform human-designed heuristics in such predictions, although it is a non-trivial task to employ Machine Learning within computational software due to the complexity of the patterns and the variability in use cases.

As we start to combine CAS computations with database fluency, the potential to apply ML to symbolic algorithm optimisation and selection are vast. This session will explore the use of ML within CAS, review some of the progress that has been made, and discuss possible future research directions.

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