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Title: Selecting the selector: Comparison of update rules for discrete global optimization

In this paper, we compare some well-known Bayesian global optimization methods in four distinct regimes, corresponding to high and low levels of measurement noise and to high and low levels of “quenched noise” (which term we use to describe the roughness of the function we are trying to optimize). We isolate the two stages of this optimization in terms of a “regressor,” which fits a model to the data measured so far, and a “selector,” which identifies the next point to be measured. Finally, the focus of this paper is to investigate the choice of selector when the regressor is well matched to the data.
Authors:
ORCiD logo [1] ;  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Texas A & M Univ., Corpus Christi, TX (United States). Dept. of Mathematics and Statistics
Publication Date:
Report Number(s):
LA-UR-16-24209
Journal ID: ISSN 1932-1864
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Statistical Analysis and Data Mining
Additional Journal Information:
Journal Volume: 10; Journal Issue: 4; Journal ID: ISSN 1932-1864
Publisher:
Wiley
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE; LANL Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; optimization; Bayesian
OSTI Identifier:
1402614
Alternate Identifier(s):
OSTI ID: 1401801