Convex Variational Optimization

Convex Variational Optimization is a variational optimization framework distinguished by the requirement that both the objective functional and the constraint set exhibit convexity, ensuring the existence of globally optimal solutions and enabling the application of convex analysis to derive necessary and sufficient optimality conditions.

87

Publications

6K

Citations

200

Authors

119

Institutions

Publications per year

2017–2026

26

Authors

200

  1. AG
    Aviv Gibali

    Braude College of Engineering Karmiel, Israel

    4 publications · 947 citations

    4

    h-index

  2. DC
    Daniel Cremers

    Technical University of Munich, Germany

    3 publications · 434 citations

    3

    h-index

  3. AY
    Aleksandr Y. Aravkin

    University of Washington, United States

    3 publications · 137 citations

    3

    h-index

  4. SO
    Stanley Osher

    University of California, Los Angeles, United States

    4 publications · 361 citations

    3

    h-index

  5. SR
    Simeon Reich

    Technion – Israel Institute of Technology, Israel

    2 publications · 910 citations

    2

    h-index

1–5 of 200

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 125 citations

    5

    h-index

  2. Applied Mathematics and Computation

    4 publications · 76 citations

    4

    h-index

  3. Communications in Mathematical Sciences

    4 publications · 316 citations

    4

    h-index

  4. SIAM Journal on Optimization

    3 publications · 287 citations

    3

    h-index

  5. 2

    h-index

1–5