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.
If you would like to give a talk please send your title and short abstract to the session organisers as soon as possible, and latest by Friday 1st March 2024 (extension applied). The short abstract will be posted on this web page once accepted.
There is an option to submit an extended abstract for the conference proceedings in Springer Lecture Notes in Computer Science to accompany the talk. This will be reviewed and if accepted, distributed during the meeting and online via Springer. The deadline to submit to these proceedings is 22nd March 2024. Details on page limits, style files and submission link are on the main ICMS webpage here: https://maths.dur.ac.uk/icms2024/ICMS2024_Registration.html.
The overall ICMS 2024 schedule is now online here. This session will take place in two parts on Thursday 25th July as timetabled below.
Chair: Johannes Hofscheier
Chair: Matthew England
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