Saddle Point Optimization
Saddle Point Optimization is a mathematical optimization framework that addresses problems involving the simultaneous minimization and maximization of a function over distinct variable sets, typically to find stable equilibria in non-convex machine learning objectives such as those in adversarial training and min-max formulations.
638
Publications
38.8K
Citations
1.2K
Authors
521
Institutions
Publications per year
2017–2026
254
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Robust Stochastic Approximation Approach to Stochastic Programming
Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan et al. · SIAM Journal on Optimization · 2009 · 2.1K citations
C. Itzykson, Jean-Bernard Zuber · Journal of Mathematical Physics · 1980 · 964 citations
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Princeton University
Princeton, United States
18 h-index
28 publications
1.1K citations
University of California, Berkeley
Berkeley, United States
15 h-index
26 publications
1.6K citations
Georgia Institute of Technology
Atlanta, United States
15 h-index
26 publications
7.2K citations
University of Illinois Urbana-Champaign
Urbana, United States
14 h-index
24 publications
1.8K citations
Massachusetts Institute of Technology
Cambridge, United States
13 h-index
25 publications
1.2K citations
The University of Texas at Austin
Austin, United States
12 h-index
22 publications
6.5K citations

University of Minnesota
Minneapolis, United States
11 h-index
25 publications
1.9K citations
University of Pennsylvania
Philadelphia, United States
11 h-index
21 publications
1.1K citations
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Repository · 99 publications · 4.3K citations
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