Thursday, 13:15 - 13:40 h, Room: H 3003A


Stefan Wild
Computational noise in simulation-based optimization


Efficient simulation of complex phenomena often results in computational noise. Noise destroys underlying smoothness that otherwise could benefit optimization algorithms. We present a non-intrusive method for estimating computational noise and show how this noise can be used to derive finite-difference estimates with provable approximation guarantees. Building upon these results, we show how step sizes for model minimization and improvement can be selected. These techniques can also be used to determine when to transition from interpolation-based to regression-based surrogate models in derivative-free optimization.


Talk 1 of the invited session Thu.2.H 3003A
"Addressing noise in derivative-free optimization" [...]
Cluster 6
"Derivative-free & simulation-based optimization" [...]


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