Type
Conference PaperDate
2020-08-20Online Publication Date
2020-08-20Print Publication Date
2021Permanent link to this record
http://hdl.handle.net/10754/665219
Metadata
Show full item recordAbstract
This paper presents an approach for the use of stochastic gradient descent methods for the solution of risk optimization problems. The first challenge is to avoid the high-cost evaluation of the failure probability and its gradient at each iteration of the optimization process. We propose here that it is accomplished by employing a stochastic gradient descent algorithm for the minimization of the Chernoff bound of the limit state function associated with the probabilistic constraint. The employed stochastic gradient descent algorithm, the Adam algorithm, is a robust method used in machine learning training. A numerical example is presented to illustrate the advantages and potential drawbacks of the proposed approach.Citation
Carlon, A. G., Torii, A. J., Lopez, R. H., & de Cursi, J. E. S. (2020). Stochastic Gradient Descent for Risk Optimization. Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling, 424–435. doi:10.1007/978-3-030-53669-5_31Publisher
Springer NatureConference/Event name
5th International Symposium on Uncertainty Quantification and Stochastic Modelling, Uncertainties 2020ISBN
9783030536688Additional Links
http://link.springer.com/10.1007/978-3-030-53669-5_31ae974a485f413a2113503eed53cd6c53
10.1007/978-3-030-53669-5_31