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
Parents
83
Amir Globerson, Sam T. Roweis · 2006 · 319 citations
Robust Mean-Covariance Solutions for Stochastic Optimization
Ioana Popescu · Operations Research · 2007 · 248 citations
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126
Technion – Israel Institute of Technology
Haifa, Israel
5 h-index
11 publications
1.3K citations

Tilburg University
Tilburg, The Netherlands
4 h-index
4 publications
273 citations
Institute of Mathematics
Hanoi, Vietnam
3 h-index
4 publications
93 citations

North China Electric Power University
Beijing, China
3 h-index
6 publications
142 citations

University of Regina
Regina, Canada
3 h-index
5 publications
218 citations
Tsinghua University
Beijing, China
3 h-index
7 publications
91 citations
Institute of Computing Technology
Beijing, China
3 h-index
6 publications
463 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
6 publications
463 citations
1–8 of 126
4 publications · 72 citations
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4 publications · 453 citations
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3 publications · 518 citations
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3 publications · 484 citations
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2 publications · 49 citations
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