Stochastic Quadratic Programming

Stochastic Quadratic Programming is a mathematical optimization framework that extends quadratic programming by incorporating uncertain parameters modeled through random variables, thereby optimizing expected objective values subject to probabilistic constraints.

72

Publications

3K

Citations

179

Authors

97

Institutions

Publications per year

2017–2026

19

Authors

179

  1. GH
    Guohe Huang

    University of Regina, Canada

    7 publications · 188 citations

    7

    h-index

  2. YL
    Yongping Li

    University of Regina, Canada

    4 publications · 90 citations

    4

    h-index

  3. SM
    Sanjay Mehrotra

    Northwestern University, United States

    2 publications · 46 citations

    2

    h-index

  4. AL
    Abdel Lisser

    Laboratoire de Recherche en Informatique, France

    2 publications · 90 citations

    2

    h-index

  5. MH
    Ming Hu

    University of Toronto, Canada

    2 publications · 40 citations

    2

    h-index

1–5 of 179

Venues

  1. Mathematical Programming

    5 publications · 142 citations

    5

    h-index

  2. SIAM Journal on Optimization

    5 publications · 174 citations

    5

    h-index

  3. Operations Research

    4 publications · 223 citations

    4

    h-index

  4. 3

    h-index

  5. Applied Mathematics and Computation

    2 publications · 47 citations

    2

    h-index

1–5