L-bfgs Optimization

L-BFGS Optimization is a limited-memory quasi-Newton method for numerical optimization that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm using a reduced set of past iterations to efficiently minimize smooth functions in high-dimensional spaces.

96

Publications

15.9K

Citations

285

Authors

127

Institutions

Publications per year

2017–2026

52

Variants

Limited-memory BfgsLimited-memory Broyden–fletcher–goldfarb–shanno Algorithm

Authors

285

  1. JN
    Jorge Nocedal

    Northwestern University, United States

    5 publications · 4.2K citations

    5

    h-index

  2. IM
    I. M. Navon

    Florida State University, United States

    3 publications · 187 citations

    3

    h-index

  3. LL
    Lin Li

    University of Electronic Science and Technology of China, China

    2 publications · 47 citations

    2

    h-index

  4. YR
    Yi Ren

    Zhejiang University, China

    2 publications · 50 citations

    2

    h-index

  5. WW
    William W. Hager

    University of Florida, United States

    2 publications · 1.7K citations

    2

    h-index

1–5 of 285

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 290 citations

    6

    h-index

  2. SIAM Journal on Optimization

    6 publications · 2.1K citations

    6

    h-index

  3. ACM Transactions on Mathematical Software

    TOMS · 3 publications · 3.9K citations

    3

    h-index

  4. Geophysics

    2 publications · 114 citations

    2

    h-index

  5. Neural Information Processing Systems

    Conference · 2 publications · 963 citations

    2

    h-index

1–5