Robust Discrete Optimization
Robust Discrete Optimization is a formal optimization framework within decision-making under uncertainty that specifically addresses discrete decision spaces by seeking solutions which remain feasible and near-optimal across a range of possible future scenarios, distinguishing it from deterministic discrete optimization and other stochastic approaches by its emphasis on worst-case performance and immunization against uncertainty.
49
Publications
4.8K
Citations
128
Authors
79
Institutions
Publications per year
2017–2026
17
Parents
49
Robust discrete optimization and network flows
Dimitris Bertsimas, Melvyn Sim · Mathematical Programming · 2003 · 1.8K citations
Robust discrete code modeling for supervised hashing
Yadan Luo, Yang Yang, Fumin Shen et al. · Pattern Recognition · 2017 · 121 citations
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79
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
7 publications
3.7K citations

Wrocław University of Science and Technology
Wroclaw, Poland
3 h-index
7 publications
118 citations
Lanzhou Jiaotong University
Lanzhou, China
2 h-index
6 publications
306 citations
University of Southampton
Southampton, United Kingdom
2 h-index
3 publications
2.2K citations

Huazhong University of Science and Technology
Wuhan, China
2 h-index
2 publications
134 citations
Singapore University of Technology and Design
Singapore, Singapore
2 h-index
2 publications
37 citations

Illinois Institute of Technology
Chicago, United States
2 h-index
3 publications
92 citations

Nanjing University of Information Science and Technology
Nanjing, China
2 h-index
2 publications
101 citations
1–8 of 79
4 publications · 289 citations
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4 publications · 1.9K citations
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OR · 3 publications · 1.1K citations
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3 publications · 139 citations
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3 publications · 127 citations
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