Penalized Optimization
Penalized Optimization is a method in plan optimization that incorporates penalty terms into the objective function to systematically discourage undesirable solution characteristics, thereby differentiating it from other optimization methods by explicitly balancing solution quality against constraint violations or complexity.
475
Publications
26.7K
Citations
933
Authors
492
Institutions
Publications per year
2017–2026
111
Parents
475
Wedgelets: nearly minimax estimation of edges
David L. Donoho · The Annals of Statistics · 1999 · 642 citations · Full text
Scaled sparse linear regression
Tao Sun, C.-H. Zhang · Biometrika · 2012 · 458 citations · Full text
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Stanford University
Stanford, United States
14 h-index
28 publications
7.1K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
13 h-index
22 publications
720 citations

Centre National de la Recherche Scientifique
Paris, France
10 h-index
19 publications
1.6K citations

Sapienza University of Rome
Rome, Italy
10 h-index
19 publications
746 citations
Wuhan University
Wuhan, China
9 h-index
21 publications
637 citations
University of California, Berkeley
Berkeley, United States
8 h-index
18 publications
943 citations
Curtin University
Perth, Australia
8 h-index
14 publications
785 citations

Chongqing Normal University
Chongqing, China
7 h-index
6 publications
212 citations
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29 publications · 2.6K citations
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Repository · 16 publications · 410 citations
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