Convex Conjugates

Convex Conjugates is a fundamental transformation in convex analysis that, given an extended real-valued function, assigns to it its supremum-based dual representation over linear perturbations, thereby generalizing the notion of a dual function in optimization.

27

Publications

2.7K

Citations

43

Authors

29

Institutions

Publications per year

2017–2026

4

Variants

Fenchel ConjugatesFenchel-legendre Transforms

Authors

43

  1. RT
    R. T. Rockafellar

    University of Washington, United States

    4 publications · 918 citations

    4

    h-index

  2. ZL
    Zongli Lin

    University of Virginia, United States

    2 publications · 112 citations

    2

    h-index

  3. MD
    M. D. Fajardo

    University of Alicante, Spain

    2 publications · 35 citations

    2

    h-index

  4. TH
    Tingshu Hu

    University of Virginia, United States

    2 publications · 112 citations

    2

    h-index

  5. AR
    Andrew R. Teel

    University of California, Santa Barbara, United States

    2 publications · 112 citations

    2

    h-index

1–5 of 43

Venues

  1. Transactions of the American Mathematical Society

    TRAN · 3 publications · 172 citations

    3

    h-index

  2. Optimization

    2 publications · 35 citations

    2

    h-index

  3. 2

    h-index

  4. IEEE Transactions on Automatic Control

    1 publications · 82 citations

    1

    h-index

  5. Mathematics of Operations Research

    1 publications · 38 citations

    1

    h-index

1–5