Linearly Constrained Quadratic Programming

Linearly Constrained Quadratic Programming is a mathematical optimization method that minimizes or maximizes a quadratic objective function subject to linear equality and inequality constraints.

68

Publications

4.1K

Citations

138

Authors

87

Institutions

Publications per year

2017–2026

6

Variants

Lcqp

Authors

138

  1. HA
    Hoai An Le Thi

    Institut National des Sciences Appliquées Rouen Normandie, France

    4 publications · 417 citations

    4

    h-index

  2. PE
    Philip E. Gill

    University of California San Diego, United States

    3 publications · 320 citations

    3

    h-index

  3. BK
    Bahman Kalantari

    Rutgers, The State University of New Jersey, United States

    2 publications · 70 citations

    2

    h-index

  4. PD
    Pham Dinh Tao

    Institut National des Sciences Appliquées Rouen Normandie, France

    2 publications · 329 citations

    2

    h-index

  5. WM
    Walter Murray

    Stanford University, United States

    2 publications · 229 citations

    2

    h-index

1–5 of 138

Venues

  1. SIAM Journal on Optimization

    8 publications · 238 citations

    8

    h-index

  2. Mathematical Programming

    7 publications · 753 citations

    7

    h-index

  3. SIAM Journal on Applied Mathematics

    3 publications · 73 citations

    3

    h-index

  4. Journal of Global Optimization

    2 publications · 298 citations

    2

    h-index

  5. Computational Optimization and Applications

    2 publications · 110 citations

    2

    h-index

1–5