Distributed Optimization

Distributed Optimization is a methodological framework within distributed algorithms that enables the collaborative minimization or maximization of a global objective function by coordinating computational agents over a network, distinguished by its decomposition of the optimization task across multiple nodes that communicate and synchronize iteratively to achieve consensus or convergence without centralized control.

2.8K

Publications

209.3K

Citations

4.6K

Authors

1.2K

Institutions

Publications per year

2017–2026

1.6K

Authors

4.6K

  1. YH
    Yiguang Hong

    Chinese Academy of Sciences, China

    53 publications · 5K citations

    35

    h-index

  2. AN
    Angelia Nedić

    University of Illinois Urbana-Champaign, United States

    48 publications · 10.7K citations

    32

    h-index

  3. AH
    Ali H. Sayed

    University of California, Los Angeles, United States

    44 publications · 3.2K citations

    25

    h-index

  4. QL
    Qing Ling

    University of Science and Technology of China, China

    36 publications · 4.8K citations

    25

    h-index

  5. AO
    Asuman Ozdaglar

    Massachusetts Institute of Technology, United States

    27 publications · 9.3K citations

    24

    h-index

1–5 of 4.6K

Venues

  1. IEEE Transactions on Automatic Control

    230 publications · 31.7K citations

    84

    h-index

  2. IEEE Transactions on Signal Processing

    104 publications · 11.1K citations

    54

    h-index

  3. arXiv (Cornell University)

    Repository · 204 publications · 9.2K citations

    43

    h-index

  4. IEEE Transactions on Cybernetics

    68 publications · 4.4K citations

    41

    h-index

  5. Automatica

    60 publications · 4.9K citations

    37

    h-index

1–5