TY - GEN
T1 - Goldbach's Function Approximation Using Deep Learning
AU - Stekel, Avigail
AU - Shukrun, Merav
AU - Azaria, Amos
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/10
Y1 - 2019/1/10
N2 - Goldbach conjecture is one of the most famous open mathematical problems. It states that every even number, bigger than two, can be presented as a sum of 2 prime numbers. In this work we present a deep learning based model that predicts the number of Goldbach partitions for a given even number. Surprisingly, our model outperforms all state-of-the-art analytically derived estimations for the number of couples, while not requiring prime factorization of the given number. We believe that building a model that can accurately predict the number of couples brings us one step closer to solving one of the world most famous open problems. To the best of our knowledge, this is the first attempt to consider machine learning based data-driven methods to approximate open mathematical problems in the field of number theory, and hope that this work will encourage such attempts.
AB - Goldbach conjecture is one of the most famous open mathematical problems. It states that every even number, bigger than two, can be presented as a sum of 2 prime numbers. In this work we present a deep learning based model that predicts the number of Goldbach partitions for a given even number. Surprisingly, our model outperforms all state-of-the-art analytically derived estimations for the number of couples, while not requiring prime factorization of the given number. We believe that building a model that can accurately predict the number of couples brings us one step closer to solving one of the world most famous open problems. To the best of our knowledge, this is the first attempt to consider machine learning based data-driven methods to approximate open mathematical problems in the field of number theory, and hope that this work will encourage such attempts.
KW - Conjecture
KW - Deep learning
KW - Mathematical problem
KW - Number theory
UR - http://www.scopus.com/inward/record.url?scp=85061905170&partnerID=8YFLogxK
U2 - 10.1109/WI.2018.00-46
DO - 10.1109/WI.2018.00-46
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AN - SCOPUS:85061905170
T3 - Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018
SP - 502
EP - 507
BT - Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018
Y2 - 3 December 2018 through 6 December 2018
ER -