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Activity Number:
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97
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #309645 |
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Title:
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Pattern Search Optimization with a Treed Gaussian Process Oracle
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Author(s):
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Matthew Taddy*+ and Genetha A. Gray and Herbert Lee and Robert Gramacy and Monica Martinez-Canales
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Companies:
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University of California, Santa Cruz and Sandia National Laboratories and University of California, Santa Cruz and University of Cambridge and Sandia National Laboratories
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Address:
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SOE Grads UCSC, Santa Cruz, CA, 95060,
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Keywords:
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optimization ; expected improvement ; convergence ; partitioning ; Gaussian process
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Abstract:
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This work combines pattern search optimization with a statistical emulator based on Treed Gaussian Processes (TGP) to create a new hybrid algorithm. The goal is to use the global probabilistic view provided by TGP to inform the local pattern search and derive a more intelligent optimization algorithm. We also propose ways in which the emulator can be used to gain information about the objective function, inform the algorithm stopping rules and provide a probabilistic analysis of the type of convergence. We present the algorithm, a framework for statistically informed optimization, and illustrate the work with numerical results.
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