Second-order Optimization

Second-Order Optimization is a gradient-based optimization method that employs second-order derivatives, specifically the Hessian matrix, to determine the curvature of the objective function and thereby achieve faster convergence compared to first-order methods.

784

Publications

57.8K

Citations

1.5K

Authors

709

Institutions

Publications per year

2017–2026

221

Variants

Second-order Optimization Methods

Authors

1.5K

  1. PL
    Philippe L. Toint

    University of Namur, Belgium

    12 publications · 725 citations

    12

    h-index

  2. NI
    Nicholas I. M. Gould

    Rutherford Appleton Laboratory, United Kingdom

    12 publications · 685 citations

    12

    h-index

  3. CC
    Coralia Cartis

    University of Oxford, United Kingdom

    9 publications · 847 citations

    9

    h-index

  4. MW
    Michael W. Mahoney

    University of California, Berkeley, United States

    7 publications · 547 citations

    7

    h-index

  5. RT
    R. T. Rockafellar

    University of Washington, United States

    7 publications · 674 citations

    7

    h-index

1–5 of 1.5K

Venues

  1. SIAM Journal on Optimization

    57 publications · 5.8K citations

    36

    h-index

  2. Mathematical Programming

    46 publications · 2.7K citations

    29

    h-index

  3. arXiv (Cornell University)

    Repository · 51 publications · 2.8K citations

    29

    h-index

  4. Journal of Optimization Theory and Applications

    51 publications · 2.7K citations

    25

    h-index

  5. Computational Optimization and Applications

    18 publications · 729 citations

    14

    h-index

1–5