2023/07/16 by Sam Blake, Blake, Sam · 1 citation
Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #History and Theory of Mathematics #Machine Learning (cs.LG) #Mathematics, Computing, and Information Processing #Number Theory (math.NT) #Polynomial and algebraic computation
paper · pdf · doi:10.48550/arxiv.2308.12290
openalex publication_date 2023/07/16 · openalex created_date 2023/08/26 · openalex updated_date 2026/07/28
In this paper we describe a deep learning--based probabilistic algorithm for integer factorisation. We use Lawrence's extension of Fermat's factorisation algorithm to reduce the integer factorisation problem to a binary classification problem. To address the classification problem, based on the ease of generating large pseudo--random primes, a corpus of training data, as large as needed, is synthetically generated. We will introduce the algorithm, summarise some experiments, analyse where these experiments fall short, and finally put out a call to others to reproduce, verify and see if this approach can be improved to a point where it becomes a practical, scalable factorisation algorithm.