Convex Optimization

Convex Optimization is a subfield of mathematical optimization that studies optimization problems in which the objective function is convex and the feasible set is convex, thereby ensuring that every local optimum is a global optimum and enabling the use of efficient solution methods.

27.8K

Publications

2.1M

Citations

30K

Authors

4.7K

Institutions

Publications per year

2017–2026

7.4K

Authors

30K

  1. SB
    Stephen Boyd

    Stanford University, United States

    111 publications · 41.8K citations

    61

    h-index

  2. RT
    R. T. Rockafellar

    University of Washington, United States

    80 publications · 15.1K citations

    50

    h-index

  3. ZL
    Zhi‐Quan Luo

    University of Minnesota, United States

    75 publications · 11K citations

    43

    h-index

  4. YY
    Yinyu Ye

    Stanford University, United States

    73 publications · 10.8K citations

    42

    h-index

  5. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    68 publications · 9.1K citations

    42

    h-index

1–5 of 30K

Venues

  1. Mathematical Programming

    1.1K publications · 131.8K citations

    162

    h-index

  2. SIAM Journal on Optimization

    1.1K publications · 119.1K citations

    151

    h-index

  3. arXiv (Cornell University)

    Repository · 1.8K publications · 87K citations

    115

    h-index

  4. IEEE Transactions on Signal Processing

    533 publications · 54.2K citations

    114

    h-index

  5. IEEE Transactions on Automatic Control

    395 publications · 45.8K citations

    105

    h-index

1–5