A Stepwise uncertainty reduction approach to constrained global optimization
International audience Using statistical emulators to guide sequential evaluations of complex computer experiments is now a well-established practice. When a model provides multiple outputs, a typical objective is to optimize one of the outputs with constraints (for instance, a threshold not to exce...
Main Author: | |
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Other Authors: | , |
Format: | Conference Object |
Language: | English |
Published: |
HAL CCSD
2014
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Subjects: | |
Online Access: | https://hal.inrae.fr/hal-02743553 https://hal.inrae.fr/hal-02743553/document https://hal.inrae.fr/hal-02743553/file/a%20stepwise%20uncertainty%20reduction%20approach%20to%20constrained%20global%20optimization_VP_1.pdf |
Summary: | International audience Using statistical emulators to guide sequential evaluations of complex computer experiments is now a well-established practice. When a model provides multiple outputs, a typical objective is to optimize one of the outputs with constraints (for instance, a threshold not to exceed) on the values of the other outputs. We propose here a new optimization strategy based on the stepwise uncertainty reduction paradigm, which o ers an e cient trade-off between exploration and local search near the boundaries. The strategy is illustrated on numerical examples. |
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