Tuesday, 13:45 - 14:10 h, Room: H 3503


Genetha Gray
Calculating and using sensitivity information during derivative-free optimization routines

Coauthors: Ethan Chan, John Guenther, Herbie Lee, John Siirola


The incorporation of uncertainty quantification (UQ) into optimization routines can help identify, characterize, reduce, and possibly eliminate uncertainty while drastically improving the usefulness of computational models and optimal solutions. Current approaches are in that they first identify optimal solutions and then, perform a series of UQ runs using these solutions. Although this approach can be effective, it can be computationally expensive or produce incomplete results. Model analysis that takes advantage of intermediate optimization iterates can reduce the expense, but the sampling done by the optimization algorithms is not ideal. In this talk, we discuss a simultaneous optimization and UQ approach that combines Bayesian statistical models and derivative-free optimization in order to monitor and use sensitivity information throughout the algorithm's execution.


Talk 2 of the invited session Tue.2.H 3503
"New techniques for optimization without derivatives" [...]
Cluster 6
"Derivative-free & simulation-based optimization" [...]


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