Large-scale Convex Optimization

Large-Scale Convex Optimization is a subfield of mathematical optimization that focuses on the theory and algorithms for solving convex optimization problems involving a large number of variables or constraints, where scalability and computational efficiency are central challenges.

1.1K

Publications

79.1K

Citations

2.2K

Authors

839

Institutions

Publications per year

2017–2026

383

Authors

2.2K

  1. PR
    Peter Richtárik

    University of Edinburgh, United Kingdom

    19 publications · 1.6K citations

    15

    h-index

  2. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    15 publications · 988 citations

    13

    h-index

  3. SB
    Stephen Boyd

    Stanford University, United States

    12 publications · 2.2K citations

    11

    h-index

  4. KT
    Kim-Chuan Toh

    National University of Singapore, Singapore

    9 publications · 449 citations

    9

    h-index

  5. FF
    Francisco Facchinei

    Sapienza University of Rome, Italy

    8 publications · 415 citations

    8

    h-index

1–5 of 2.2K

Venues

  1. Mathematical Programming

    72 publications · 7.4K citations

    42

    h-index

  2. arXiv (Cornell University)

    Repository · 146 publications · 5.7K citations

    41

    h-index

  3. SIAM Journal on Optimization

    55 publications · 6.9K citations

    36

    h-index

  4. Journal of Optimization Theory and Applications

    44 publications · 2.7K citations

    25

    h-index

  5. Computational Optimization and Applications

    26 publications · 1.3K citations

    19

    h-index

1–5