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
166
1–5 of 166
377
8
h-index
6
h-index
4
h-index
Rutgers, The State University of New Jersey, United States
4 publications · 427 citations
4
h-index
4
h-index
1–5 of 377
193
Georgia Institute of Technology
Atlanta, United States
9 h-index
16 publications
1.2K citations
Carnegie Mellon University
Pittsburgh, United States
6 h-index
12 publications
836 citations
Northwestern University
Evanston, United States
6 h-index
11 publications
1.1K citations
University of Michigan
Ann Arbor, United States
5 h-index
11 publications
1.5K citations
University of Wisconsin–Madison
Madison, United States
5 h-index
5 publications
502 citations
Norwegian University of Science and Technology
Trondheim, Norway
5 h-index
8 publications
309 citations
The Ohio State University
Columbus, United States
5 h-index
8 publications
498 citations
University of Arizona
Tucson, United States
5 h-index
10 publications
1.3K citations
1–8 of 193
13 publications · 1.7K citations
13
h-index
8 publications · 449 citations
8
h-index
7 publications · 1K citations
7
h-index
7 publications · 479 citations
7
h-index
8 publications · 982 citations
7
h-index
1–5