Stochastic Constrained Optimization
Stochastic Constrained Optimization is a mathematical optimization framework that belongs to the class of constrained optimization problems, distinguished by the incorporation of random variables or stochastic processes in the objective function or constraints, thereby requiring solutions that account for uncertainty while satisfying feasibility conditions in expectation or with high probability.
187
Publications
8.8K
Citations
518
Authors
254
Institutions
Publications per year
2017–2026
76
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187
1–5 of 187
518
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254
Tsinghua University
Beijing, China
7 h-index
20 publications
2.3K citations

Sun Yat-sen University
Guangzhou, China
7 h-index
23 publications
1.4K citations
Rutgers, The State University of New Jersey
New Brunswick, United States
6 h-index
9 publications
232 citations
Georgia Institute of Technology
Atlanta, United States
5 h-index
10 publications
291 citations

Zhejiang University
Hangzhou, China
4 h-index
9 publications
349 citations

Cornell University
Ithaca, United States
4 h-index
4 publications
324 citations
Technion – Israel Institute of Technology
Haifa, Israel
4 h-index
4 publications
272 citations

City University of Hong Kong
Hong Kong, Hong Kong
4 h-index
8 publications
168 citations
1–8 of 254
7 publications · 110 citations
7
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6 publications · 273 citations
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5 publications · 158 citations
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4 publications · 274 citations
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TSTE · 4 publications · 281 citations
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