Journal of Advances in Applied Mathematics

Download PDF (655.5 KB) PP. 1 - 14 Pub. Date: January 1, 2017

DOI: 10.22606/jaam.2017.21001

• John Guenther^*
  Division of Applied Mathematics and Statistics, University of California, Santa Cruz, United States
• Herbert Lee
  Division of Applied Mathematics and Statistics, University of California, Santa Cruz, United States

The black box functions found in computer experiments often have multiple optima. When there are multiple optima, the goal of a computer experiment should be to determine the best optimum for the purposes of the experiment. This brings up the concept of the utility of an optimum: The degree to which the optimum fulfills the purposes of the experiment. The utility of an optima is based on measures of the variability of the surface in the tolerance region (region of interest around the optima) defined by the accuracy with which influential variables can be specified. This tolerance region can be symmetric or asymmetric. For symmetric optima the tolerance region that provides maximum utility is centered at the optimum point. For asymmetric optima, it may be advantageous to displace the center of the tolerance region from the optimum point. For example, a very skewed optimum (minimum in this case) can increase significantly in one direction but only slightly in the opposite direction from the minimum point. For such an optimum the center point of the tolerance region that provides maximum utility for the optimum is displaced from the optimum point along the direction that only slightly increases. The algorithm discussed in this paper locates the center point for the tolerance region to achieve the best utility for any optimum, symmetric or asymmetric. Making use of the surface predictions of the emulator around the minimum point, it employs pattern search to find the best center point for any optimum and for any dimensionality.

Bayesian statistics, treed gaussian process, emulator, decision theory, optimization.

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