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Creators/Authors contains: "Xu, Yinzhan"

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  1. ; ; (, Proceedings of Machine Learning Research)
    Free, publicly-accessible full text available July 4, 2026
  2. ; ; ; (, Society for Industrial and Applied Mathematics)
    Free, publicly-accessible full text available January 1, 2026
  3. ; ; (, Proceedings of SODA (Symposium on Discrete Algorithms))
    Accepted Manuscript Full Text Available
  4. ; ; ; (, ACM)
    Full Text Available 
  5. ; ; ; (, Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA))
    The main contribution of this paper is a new improved variant of the laser method for designing matrix multiplication algorithms. Building upon the recent techniques of [Duan, Wu, Zhou, FOCS 2023], the new method introduces several new ingredients that not only yield an improved bound on the matrix multiplication exponent ω, but also improve the known bounds on rectangular matrix multiplication by [Le Gall and Urrutia, SODA 2018]. In particular, the new bound on ω is ω ≤ 2.371552 (improved from ω ≤ 2.371866). For the dual matrix multiplication exponent α defined as the largest α for which ω(1, α, 1) = 2, we obtain the improvement α ≥ 0.321334 (improved from α ≥ 0.31389). Similar improvements are obtained for various other exponents for multiplying rectangular matrices. 
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    Accepted Manuscript Full Text Available
  6. ; (, Proc. 7th SIAM Sympos. Simplicity of Algorithms (SOSA))
    Full Text Available 
  7. ; ; (, SIAM)
    Accepted Manuscript Full Text Available
  8. ; ; ; (, Proc. 64th IEEE Sympos. Foundations of Computer Science (FOCS))
    Full Text Available 
  9. ; ; (, Proc. 55th ACM Sympos. Theory of Computing (STOC))
    Full Text Available 
  10. ; ; (, 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS))
    Full Text Available