JOURNAL ARTICLE

Smooth Subsum Search A Heuristic for Practical Integer Factorization.

  • Published In: International Journal of Foundations of Computer Science, 2024, v. 35, n. 8. P. 949 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hittmeir, Markus 3 of 3

Abstract

The two currently fastest general-purpose integer factorization algorithms are the Quadratic Sieve and the Number Field Sieve. Both techniques are used to find so-called smooth values of certain polynomials, i.e., values that factor completely over a set of small primes (the factor base). As the names of the methods suggest, a sieving procedure is used for the task of quickly identifying smooth values among the candidates in a certain range. While the Number Field Sieve is asymptotically faster, the Quadratic Sieve is still considered the most efficient factorization technique for numbers up to around 100 digits. In this paper, we challenge the Quadratic Sieve by presenting a novel approach based on representing smoothness candidates as sums that are always divisible by several of the primes in the factor base. The resulting values are generally smaller than those considered in the Quadratic Sieve, increasing the likelihood of them being smooth. Using the fastest implementations of the Self-initializing Quadratic Sieve in Python as benchmarks, a Python implementation of our approach runs consistently 5 to 7 times faster for numbers with 45–100 digits, and around 10 times faster for numbers with 30–40 digits. We discuss several avenues for further improvements and applications of the technique. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Foundations of Computer Science. 2024/12, Vol. 35, Issue 8, p949
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0129-0541
  • DOI:10.1142/S0129054123500296
  • Accession Number:181731581
  • Copyright Statement:Copyright of International Journal of Foundations of Computer Science is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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