Parallel Eigenvalue Computation

Parallel Eigenvalue Computation is a computational method within numerical linear algebra that determines eigenvalues of large matrices by simultaneously distributing and processing subproblems across multiple processors to accelerate convergence.

271

Publications

12.4K

Citations

525

Authors

240

Institutions

Publications per year

2017–2026

21

Authors

525

  1. JD
    James Demmel

    University of California, Berkeley, United States

    11 publications · 513 citations

    11

    h-index

  2. JD
    Jack Dongarra

    University of Tennessee at Knoxville, United States

    12 publications · 409 citations

    11

    h-index

  3. AS
    Ahmed Sameh

    Purdue University West Lafayette, United States

    8 publications · 528 citations

    8

    h-index

  4. CY
    Chao Yang

    Lawrence Berkeley National Laboratory, United States

    7 publications · 323 citations

    7

    h-index

  5. BL
    Bruno Lang

    University of Wuppertal, Germany

    6 publications · 288 citations

    6

    h-index

1–5 of 525

Venues

  1. SIAM Journal on Scientific Computing

    31 publications · 1.2K citations

    21

    h-index

  2. Parallel Computing

    21 publications · 646 citations

    14

    h-index

  3. SIAM Journal on Matrix Analysis and Applications

    10 publications · 1.5K citations

    10

    h-index

  4. 9

    h-index

  5. ACM Transactions on Mathematical Software

    TOMS · 7 publications · 218 citations

    7

    h-index

1–5