Jacobian Matrix Approximation

Jacobian Matrix Approximation is a numerical method that estimates the Jacobian matrix of a multivariable function by finite differences or other computational techniques to facilitate convergence in iterative solvers when exact derivatives are unavailable or costly to compute.

398

Publications

20.7K

Citations

1K

Authors

483

Institutions

Publications per year

2017–2026

82

Authors

1K

  1. MY
    Mohammed Yusuf Waziri

    Bayero University Kano, Nigeria

    9 publications · 141 citations

    9

    h-index

  2. CC
    Chien Chern Cheah

    Nanyang Technological University, Singapore

    6 publications · 453 citations

    6

    h-index

  3. DW
    David W. Zingg

    University of Toronto, Canada

    7 publications · 190 citations

    5

    h-index

  4. TF
    Thomas F. Coleman

    Cornell University, United States

    5 publications · 676 citations

    5

    h-index

  5. MK
    Masahiro Kinoshita

    Kyoto University, Japan

    4 publications · 211 citations

    4

    h-index

1–5 of 1K

Venues

  1. IEEE Transactions on Power Systems

    12 publications · 779 citations

    12

    h-index

  2. SIAM Journal on Numerical Analysis

    12 publications · 1.8K citations

    11

    h-index

  3. Mathematics of Computation

    MCOM · 9 publications · 636 citations

    9

    h-index

  4. SIAM Journal on Scientific Computing

    9 publications · 518 citations

    9

    h-index

  5. 8

    h-index

1–5