Sparse Spectral Approximations

Sparse Spectral Approximations is a computational framework within spectral methods that leverages sparsity-promoting techniques to efficiently approximate eigenfunctions or spectral representations of differential operators while retaining high accuracy with reduced degrees of freedom.

132

Publications

9.9K

Citations

304

Authors

163

Institutions

Publications per year

2017–2026

27

Authors

304

  1. DA
    Daniel A. Spielman

    Yale University, United States

    6 publications · 1.2K citations

    6

    h-index

  2. NS
    Nikhil Srivastava

    Yale University, United States

    5 publications · 1.2K citations

    5

    h-index

  3. JS
    Jie Shen

    Purdue University West Lafayette, United States

    4 publications · 348 citations

    4

    h-index

  4. KR
    Kannan Ramchandran

    University of California, Berkeley, United States

    3 publications · 117 citations

    3

    h-index

  5. AL
    A. Levin

    Massachusetts Institute of Technology, United States

    3 publications · 108 citations

    3

    h-index

1–5 of 304

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 147 citations

    6

    h-index

  2. Journal of Computational Physics

    6 publications · 127 citations

    6

    h-index

  3. Linear Algebra and its Applications

    4 publications · 230 citations

    4

    h-index

  4. Numerische Mathematik

    4 publications · 190 citations

    4

    h-index

  5. SIAM Journal on Numerical Analysis

    3 publications · 189 citations

    3

    h-index

1–5