Bregman Optimization

Bregman Optimization is a variational optimization method that generalizes gradient-based updates by replacing Euclidean distance measures with Bregman divergences to exploit the geometric structure of the underlying parameter space.

195

Publications

15.2K

Citations

452

Authors

239

Institutions

Publications per year

2017–2026

59

Authors

452

  1. SO
    Stanley Osher

    University of California, Los Angeles, United States

    14 publications · 3.4K citations

    13

    h-index

  2. WY
    Wotao Yin

    University of California, Los Angeles, United States

    6 publications · 2K citations

    6

    h-index

  3. YY
    Yunyun Yang

    Harbin Institute of Technology, China

    6 publications · 151 citations

    6

    h-index

  4. BW
    Boying Wu

    Harbin Institute of Technology, China

    5 publications · 139 citations

    5

    h-index

  5. FN
    Frank Nielsen

    Sony Computer Science Laboratories, Japan

    5 publications · 435 citations

    5

    h-index

1–5 of 452

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 410 citations

    11

    h-index

  2. Inverse Problems

    8 publications · 254 citations

    8

    h-index

  3. SIAM Journal on Imaging Sciences

    SIIMS · 7 publications · 3.2K citations

    7

    h-index

  4. IEEE Transactions on Information Theory

    6 publications · 838 citations

    6

    h-index

  5. Optimization

    5 publications · 317 citations

    5

    h-index

1–5