Robust Quadratic Programming

Robust Quadratic Programming is a methodological framework within optimization theory that extends standard quadratic programming by incorporating uncertainty sets in the problem parameters and seeking solutions that remain feasible and optimal under worst-case perturbations.

83

Publications

4.6K

Citations

212

Authors

126

Institutions

Publications per year

2017–2026

20

Authors

212

  1. AB
    Aharon Ben‐Tal

    Technion – Israel Institute of Technology, Israel

    6 publications · 528 citations

    6

    h-index

  2. GH
    Guohe Huang

    University of Regina, Canada

    4 publications · 119 citations

    4

    h-index

  3. YL
    Yongping Li

    University of Regina, Canada

    3 publications · 63 citations

    3

    h-index

  4. AN
    Arkadi Nemirovski

    Technion – Israel Institute of Technology, Israel

    3 publications · 431 citations

    3

    h-index

  5. XL
    Xinwei Liu

    Chinese Academy of Sciences, China

    2 publications · 122 citations

    2

    h-index

1–5 of 212

Venues

  1. Journal of Global Optimization

    4 publications · 72 citations

    4

    h-index

  2. SIAM Journal on Optimization

    4 publications · 453 citations

    4

    h-index

  3. IEEE Transactions on Power Systems

    3 publications · 518 citations

    3

    h-index

  4. Mathematical Programming

    3 publications · 484 citations

    3

    h-index

  5. IFAC-PapersOnLine

    2 publications · 49 citations

    2

    h-index

1–5