Dual Linear Programming
Dual Linear Programming is a mathematical optimization framework that, for a given linear programming problem, formulates a corresponding dual problem in which the objective and constraints are transformed such that the dual variables represent the marginal values of the primal constraints, thereby providing an alternative characterization of the same underlying optimization structure.
339
Publications
17.4K
Citations
608
Authors
328
Institutions
Publications per year
2017–2026
29
Variants
Dual Lp
Parents
339
Regression Rank Scores and Regression Quantiles
Christoph Gutenbrünner, Jana Jurečková · The Annals of Statistics · 1992 · 271 citations · Full text
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Rutgers, The State University of New Jersey, United States
4 publications · 300 citations
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328
Massachusetts Institute of Technology
Cambridge, United States
12 h-index
23 publications
2.8K citations
Rutgers, The State University of New Jersey
New Brunswick, United States
10 h-index
16 publications
847 citations
Stanford University
Stanford, United States
8 h-index
10 publications
576 citations
Hebrew University of Jerusalem
Jerusalem, Israel
6 h-index
7 publications
284 citations

Cornell University
Ithaca, United States
6 h-index
9 publications
465 citations
Georgia Institute of Technology
Atlanta, United States
6 h-index
10 publications
348 citations
University of California, Berkeley
Berkeley, United States
5 h-index
6 publications
465 citations

Sapienza University of Rome
Rome, Italy
5 h-index
5 publications
117 citations
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14 publications · 323 citations
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13 publications · 448 citations
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15 publications · 1.2K citations
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14 publications · 627 citations
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10 publications · 1.4K citations
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