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
Parents
68
1–5 of 68
138
Institut National des Sciences Appliquées Rouen Normandie, France
4 publications · 417 citations
4
h-index
3
h-index
Rutgers, The State University of New Jersey, United States
2 publications · 70 citations
2
h-index
Institut National des Sciences Appliquées Rouen Normandie, France
2 publications · 329 citations
2
h-index
2
h-index
1–5 of 138
87

Institut National des Sciences Appliquées Rouen Normandie
Saint-Étienne-du-Rouvray, France
4 h-index
10 publications
900 citations

Université de Rouen Normandie
Rouen, France
3 h-index
8 publications
768 citations

University of Parma
Parma, Italy
2 h-index
3 publications
63 citations
Stanford University
Stanford, United States
2 h-index
3 publications
483 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
2 h-index
2 publications
84 citations
Institute of Mathematics
Hanoi, Vietnam
2 h-index
3 publications
81 citations
University of California, Los Angeles
Los Angeles, United States
2 h-index
2 publications
116 citations

Centre National de la Recherche Scientifique
Paris, France
2 h-index
4 publications
702 citations
1–8 of 87
8 publications · 238 citations
8
h-index
7 publications · 753 citations
7
h-index
3 publications · 73 citations
3
h-index
2 publications · 298 citations
2
h-index
2 publications · 110 citations
2
h-index
1–5