Hessian Approximation

Hessian Approximation is a computational method in gradient-based optimization that approximates the Hessian matrix to improve convergence properties while avoiding the high computational cost of exact Hessian evaluation.

1.8K

Publications

172.2K

Citations

3.6K

Authors

1.3K

Institutions

Publications per year

2017–2026

518

Authors

3.6K

  1. JN
    Jorge Nocedal

    Northwestern University, United States

    16 publications · 11.6K citations

    17

    h-index

  2. RH
    Richard H. Byrd

    University of Colorado Boulder, United States

    15 publications · 11.5K citations

    15

    h-index

  3. MW
    Michael W. Mahoney

    University of California, Berkeley, United States

    15 publications · 1.3K citations

    14

    h-index

  4. PL
    Philippe L. Toint

    University of Namur, Belgium

    11 publications · 1.6K citations

    11

    h-index

  5. LT
    Lorenz T. Biegler

    Carnegie Mellon University, United States

    9 publications · 629 citations

    10

    h-index

1–5 of 3.6K

Venues

  1. The Journal of Chemical Physics

    108 publications · 13.2K citations

    49

    h-index

  2. Mathematical Programming

    69 publications · 7.2K citations

    39

    h-index

  3. arXiv (Cornell University)

    Repository · 92 publications · 8K citations

    33

    h-index

  4. Journal of Optimization Theory and Applications

    67 publications · 3.5K citations

    31

    h-index

  5. SIAM Journal on Optimization

    53 publications · 6.6K citations

    29

    h-index

1–5