References in zbMATH (referenced in 77 articles )

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  1. Censor, Yair; Garduño, Edgar; Helou, Elias S.; Herman, Gabor T.: Derivative-free superiorization: principle and algorithm (2021)
  2. Chu, Fei; Cheng, Xiang; Peng, Chuang; Jia, Runda; Chen, Tao; Wei, Qinglai: A process transfer model-based optimal compensation control strategy for batch process using just-in-time learning and trust region method (2021)
  3. Jones, Donald R.; Martins, Joaquim R. R. A.: The DIRECT algorithm: 25 years later (2021)
  4. Stripinis, Linas; Paulavičius, Remigijus: A new \textttDIRECT-GLh algorithm for global optimization with hidden constraints (2021)
  5. Stripinis, Linas; Žilinskas, Julius; Casado, Leocadio G.; Paulavičius, Remigijus: On \textttMATLABexperience in accelerating \textttDIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization (2021)
  6. Ahmed, Mohamed Osama; Vaswani, Sharan; Schmidt, Mark: Combining Bayesian optimization and Lipschitz optimization (2020)
  7. Akimoto, Youhei; Auger, Anne; Hansen, Nikolaus: Quality gain analysis of the weighted recombination evolution strategy on general convex quadratic functions (2020)
  8. Bajaj, Ishan; Faruque Hasan, M. M.: Deterministic global derivative-free optimization of black-box problems with bounded Hessian (2020)
  9. Bemporad, Alberto: Global optimization via inverse distance weighting and radial basis functions (2020)
  10. Binois, Mickaël; Ginsbourger, David; Roustant, Olivier: On the choice of the low-dimensional domain for global optimization via random embeddings (2020)
  11. Hare, Warren: A discussion on variational analysis in derivative-free optimization (2020)
  12. Kiatsupaibul, Seksan; Smith, Robert L.; Zabinsky, Zelda B.: Single observation adaptive search for discrete and continuous stochastic optimization (2020)
  13. Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
  14. Manno, Andrea; Amaldi, Edoardo; Casella, Francesco; Martelli, Emanuele: A local search method for costly black-box problems and its application to CSP plant start-up optimization refinement (2020)
  15. Paradezhenko, G. V.; Melnikov, N. B.; Reser, B. I.: Numerical continuation method for nonlinear system of scalar and functional equations (2020)
  16. Pozharskiy, Dmitry; Wichrowski, Noah J.; Duncan, Andrew B.; Pavliotis, Grigorios A.; Kevrekidis, Ioannis G.: Manifold learning for accelerating coarse-grained optimization (2020)
  17. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  18. Bei, Xiaoqiang; Zhu, Xiaoyan; Coit, David W.: A risk-averse stochastic program for integrated system design and preventive maintenance planning (2019)
  19. Berahas, Albert S.; Byrd, Richard H.; Nocedal, Jorge: Derivative-free optimization of noisy functions via quasi-Newton methods (2019)
  20. Censor, Yair; Heaton, Howard; Schulte, Reinhard: Derivative-free superiorization with component-wise perturbations (2019)

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