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

Authors

608

  1. MJ
    Michael J. Todd

    Cornell University, United States

    5 publications · 273 citations

    5

    h-index

  2. TJ
    Tommi Jaakkola

    Massachusetts Institute of Technology, United States

    5 publications · 435 citations

    5

    h-index

  3. YY
    Yinyu Ye

    Stanford University, United States

    5 publications · 279 citations

    5

    h-index

  4. AP
    András Prékopa

    Rutgers, The State University of New Jersey, United States

    4 publications · 300 citations

    4

    h-index

  5. AL
    Andreas Löhne

    Martin Luther University Halle-Wittenberg, Germany

    4 publications · 133 citations

    4

    h-index

1–5 of 608

Venues

  1. Journal of Optimization Theory and Applications

    14 publications · 323 citations

    13

    h-index

  2. 13

    h-index

  3. Mathematical Programming

    15 publications · 1.2K citations

    13

    h-index

  4. Mathematics of Operations Research

    14 publications · 627 citations

    12

    h-index

  5. Operations Research

    10 publications · 1.4K citations

    10

    h-index

1–5