Stochastic Programming Decomposition

Stochastic Programming Decomposition is a computational method in optimization under uncertainty that decomposes large-scale stochastic programs into smaller, more manageable subproblems by exploiting the inherent scenario-based or stage-based structure of the problem, thereby enabling efficient solution through iterative coordination of scenario-specific and recourse decisions.

166

Publications

13.9K

Citations

377

Authors

193

Institutions

Publications per year

2017–2026

54

Authors

377

  1. SS
    Suvrajeet Sen

    University of Arizona, United States

    8 publications · 803 citations

    8

    h-index

  2. SA
    Shabbir Ahmed

    Georgia Institute of Technology, United States

    6 publications · 586 citations

    6

    h-index

  3. FY
    Fengqi You

    Cornell University, United States

    4 publications · 625 citations

    4

    h-index

  4. AR
    Andrzej Ruszczyński

    Rutgers, The State University of New Jersey, United States

    4 publications · 427 citations

    4

    h-index

  5. MG
    Michel Gendreau

    Université de Montréal, Canada

    4 publications · 188 citations

    4

    h-index

1–5 of 377

Venues

  1. IEEE Transactions on Power Systems

    13 publications · 1.7K citations

    13

    h-index

  2. INFORMS journal on computing

    8 publications · 449 citations

    8

    h-index

  3. Mathematical Programming

    7 publications · 1K citations

    7

    h-index

  4. SIAM Journal on Optimization

    7 publications · 479 citations

    7

    h-index

  5. Operations Research

    8 publications · 982 citations

    7

    h-index

1–5