References in zbMATH (referenced in 111 articles )

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  1. Byrka, Jaroslaw; Srinivasan, Aravind: Approximation algorithms for stochastic and risk-averse optimization (2018)
  2. Chung, Julianne; Español, Malena I.: Learning regularization parameters for general-form Tikhonov (2017)
  3. Delgado de Oliveira, Alan; Filomena, Tiago Pascoal; Scherer Perlin, Marcelo; Lejeune, Miguel; de Macedo, Guilherme Ribeiro: A multistage stochastic programming asset-liability management model: an application to the Brazilian pension fund industry (2017)
  4. Zéphyr, Luckny; Lang, Pascal; Lamond, Bernard F.; C^oté, Pascal: Approximate stochastic dynamic programming for hydroelectric production planning (2017)
  5. Cao, Yankai; Laird, Carl D.; Zavala, Victor M.: Clustering-based preconditioning for stochastic programs (2016)
  6. Chen, Shuang; Pang, Li-Ping; Ma, Xue-Fei; Li, Dan: SAA method based on modified Newton method for stochastic variational inequality with second-order cone constraints and application in portfolio optimization (2016)
  7. Löhndorf, Nils: An empirical analysis of scenario generation methods for stochastic optimization (2016)
  8. Mitra, Sumit; Garcia-Herreros, Pablo; Grossmann, Ignacio E.: A cross-decomposition scheme with integrated primal-dual multi-cuts for two-stage stochastic programming investment planning problems (2016)
  9. Sen, Suvrajeet; Liu, Yifan: Mitigating uncertainty via compromise decisions in two-stage stochastic linear programming: variance reduction (2016)
  10. Stockbridge, Rebecca; Bayraksan, Güzin: Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming (2016)
  11. Aldasoro, Unai; Escudero, Laureano F.; Merino, María; Monge, Juan F.; Pérez, Gloria: On parallelization of a stochastic dynamic programming algorithm for solving large-scale mixed $0-1$ problems under uncertainty (2015)
  12. Carè, A.; Garatti, S.; Campi, M.C.: Scenario MIN-MAX optimization and the risk of empirical costs (2015)
  13. Cotton, Tanisha G.; Ntaimo, Lewis: Computational study of decomposition algorithms for mean-risk stochastic linear programs (2015)
  14. He, Suxiang; Wei, Min; Tong, Hengqing: A smooth penalty-based sample average approximation method for stochastic complementarity problems (2015)
  15. Jenabi, M.; Fatemi Ghomi, S.M.T.; Torabi, S.A.; Hosseinian, S.H.: Acceleration strategies of Benders decomposition for the security constraints power system expansion planning (2015)
  16. Lan, Guanghui: Bundle-level type methods uniformly optimal for smooth and nonsmooth convex optimization (2015)
  17. Parpas, Panos; Ustun, Berk; Webster, Mort; Tran, Quang Kha: Importance sampling in stochastic programming: a Markov chain Monte Carlo approach (2015)
  18. Rossi, Roberto; Hnich, Brahim; Tarim, S.Armagan; Prestwich, Steven: Confidence-based reasoning in stochastic constraint programming (2015)
  19. Song, Yongjia; Luedtke, James: An adaptive partition-based approach for solving two-stage stochastic programs with fixed recourse (2015)
  20. Espinoza, Daniel; Moreno, Eduardo: A primal-dual aggregation algorithm for minimizing conditional value-at-risk in linear programs (2014)

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