Algorithms for stochastic optimization with function or expectation constraints
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DOI: 10.1007/s10589-020-00179-x
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- D. Goldfarb & G. Iyengar, 2003. "Robust Portfolio Selection Problems," Mathematics of Operations Research, INFORMS, vol. 28(1), pages 1-38, February.
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- Lingzi Jin & Xiao Wang, 2022. "A stochastic primal-dual method for a class of nonconvex constrained optimization," Computational Optimization and Applications, Springer, vol. 83(1), pages 143-180, September.
- Liwei Zhang & Yule Zhang & Jia Wu & Xiantao Xiao, 2022. "Solving Stochastic Optimization with Expectation Constraints Efficiently by a Stochastic Augmented Lagrangian-Type Algorithm," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2989-3006, November.
- Yuanyuan, Zhang & Huiru, Zhao & Bingkang, Li, 2023. "Distributionally robust comprehensive declaration strategy of virtual power plant participating in the power market considering flexible ramping product and uncertainties," Applied Energy, Elsevier, vol. 343(C).
- Bo Wei & William B. Haskell & Sixiang Zhao, 2020. "The CoMirror algorithm with random constraint sampling for convex semi-infinite programming," Annals of Operations Research, Springer, vol. 295(2), pages 809-841, December.
- Drew P. Kouri & Mathias Staudigl & Thomas M. Surowiec, 2023. "A relaxation-based probabilistic approach for PDE-constrained optimization under uncertainty with pointwise state constraints," Computational Optimization and Applications, Springer, vol. 85(2), pages 441-478, June.
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Keywords
Convex programming; Stochastic optimization; Complexity; Subgradient method;All these keywords.
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