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A Machine Learning Based Software Pipeline to Pick the Variable Ordering for Algorithms with Polynomial Inputs
[chapter]
2020
Lecture Notes in Computer Science
We are interested in the application of Machine Learning (ML) technology to improve mathematical software. It may seem that the probabilistic nature of ML tools would invalidate the exact results prized by such software, however, the algorithms which underpin the software often come with a range of choices which are good candidates for ML application. We refer to choices which have no effect on the mathematical correctness of the software, but do impact its performance. In the past we
doi:10.1007/978-3-030-52200-1_30
fatcat:mkrpckkoxnevpdiynqfwny6rhu