Selecting the selector: Comparison of update rules for discrete global optimization
Abstract
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:
-
[1];
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Texas A & M Univ., Corpus Christi, TX (United States). Dept. of Mathematics and Statistics
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE; LANL Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1402614
- Alternate Identifier(s):
- OSTI ID: 1401801
- Report Number(s):
- LA-UR-16-24209
Journal ID: ISSN 1932-1864
- Grant/Contract Number:
- AC52-06NA25396
- Resource 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
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; optimization; Bayesian
Citation Formats
Theiler, James, and Zimmer, Beate G. Selecting the selector: Comparison of update rules for discrete global optimization. United States: N. p., 2017.
Web. doi:10.1002/sam.11343.
Theiler, James, & Zimmer, Beate G. Selecting the selector: Comparison of update rules for discrete global optimization. United States. https://doi.org/10.1002/sam.11343
Theiler, James, and Zimmer, Beate G. Wed .
"Selecting the selector: Comparison of update rules for discrete global optimization". United States. https://doi.org/10.1002/sam.11343. https://www.osti.gov/servlets/purl/1402614.
@article{osti_1402614,
title = {Selecting the selector: Comparison of update rules for discrete global optimization},
author = {Theiler, James and Zimmer, Beate G.},
abstractNote = {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.},
doi = {10.1002/sam.11343},
journal = {Statistical Analysis and Data Mining},
number = 4,
volume = 10,
place = {United States},
year = {Wed May 24 00:00:00 EDT 2017},
month = {Wed May 24 00:00:00 EDT 2017}
}
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