Dense Matrix Algorithms

Dense Matrix Algorithms is a class of computational methods in numerical linear algebra designed specifically for matrices in which most elements are non-zero, distinguishing them from sparse matrix algorithms by their assumption of high data density and their use of full-storage, level-3 BLAS operations to achieve high performance on dense data structures.

352

Publications

28.3K

Citations

750

Authors

287

Institutions

Publications per year

2017–2026

42

Authors

750

  1. JD
    Jack Dongarra

    University of Tennessee at Knoxville, United States

    26 publications · 1.8K citations

    20

    h-index

  2. RA
    Robert A. Geijn

    The University of Texas at Austin, United States

    16 publications · 729 citations

    13

    h-index

  3. JD
    James Demmel

    University of California, Berkeley, United States

    9 publications · 1.6K citations

    9

    h-index

  4. ST
    Stanimire Tomov

    University of Tennessee at Knoxville, United States

    9 publications · 665 citations

    9

    h-index

  5. ES
    Enrique S. Quintana–Ort́ı

    Universitat Jaume I, Spain

    9 publications · 428 citations

    9

    h-index

1–5 of 750

Venues

  1. ACM Transactions on Mathematical Software

    TOMS · 28 publications · 6K citations

    20

    h-index

  2. SIAM Journal on Scientific Computing

    16 publications · 1.1K citations

    15

    h-index

  3. SIAM Journal on Matrix Analysis and Applications

    10 publications · 1.5K citations

    10

    h-index

  4. arXiv (Cornell University)

    Repository · 6 publications · 133 citations

    6

    h-index

  5. Parallel Computing

    6 publications · 72 citations

    6

    h-index

1–5