Sublinear-time Linear Algebra

Sublinear-Time Linear Algebra is a computational framework within the field of sublinear algorithms that enables the approximation of fundamental linear algebra operations—such as matrix multiplication, norm estimation, and low-rank approximation—in time sublinear in the size of the input matrices, thereby distinguishing itself from classical linear algebra algorithms by prioritizing computational efficiency over exact computation.

8

Publications

672

Citations

9

Authors

8

Institutions

Publications per year

2017–2026

4

Authors

9

  1. YT
    Yin Tat Lee

    University of Washington, United States

    3 publications · 307 citations

    3

    h-index

  2. ZS
    Zhao Song

    The University of Texas at Austin, United States

    3 publications · 307 citations

    3

    h-index

  3. MB
    Michael B. Cohen

    Massachusetts Institute of Technology, United States

    3 publications · 307 citations

    3

    h-index

  4. MB
    Markus Bläser

    University of Lübeck, Germany

    1 publications · 26 citations

    1

    h-index

  5. PI
    Piotr Indyk

    Massachusetts Institute of Technology, United States

    1 publications · 202 citations

    1

    h-index

1–5 of 9

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 53 citations

    3

    h-index

  2. Journal of the ACM

    JACM · 1 publications · 132 citations

    1

    h-index

  3. Computational Complexity

    CC · 1 publications · 26 citations

    1

    h-index

  4. 1

    h-index

1–4