Randomized Matrix Algorithms

Randomized Matrix Algorithms is a class of computational methods that employ randomness as a fundamental mechanism to achieve efficient approximation or decomposition of matrix operations, distinguishing itself from deterministic linear algebra techniques by leveraging probabilistic constructs to reduce complexity in high-dimensional settings.

320

Publications

23.8K

Citations

503

Authors

233

Institutions

Publications per year

2017–2026

88

Authors

503

  1. DP
    David P. Woodruff

    IBM Research - Almaden, United States

    18 publications · 1.5K citations

    15

    h-index

  2. PM
    Per‐Gunnar Martinsson

    University of Colorado Boulder, United States

    13 publications · 5.4K citations

    12

    h-index

  3. PD
    Petros Drineas

    Rensselaer Polytechnic Institute, United States

    14 publications · 1.8K citations

    12

    h-index

  4. MW
    Michael W. Mahoney

    University of California, Berkeley, United States

    13 publications · 1.9K citations

    12

    h-index

  5. CB
    Christos Boutsidis

    Rensselaer Polytechnic Institute, United States

    11 publications · 933 citations

    11

    h-index

1–5 of 503

Venues

  1. 20

    h-index

  2. arXiv (Cornell University)

    Repository · 45 publications · 1.5K citations

    19

    h-index

  3. Linear Algebra and its Applications

    12 publications · 510 citations

    12

    h-index

  4. SIAM Journal on Scientific Computing

    10 publications · 593 citations

    10

    h-index

  5. ArXiv.org

    Repository · 6 publications · 248 citations

    6

    h-index

1–5