DagSemProc.09181.2.pdf
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09181 Executive Summary – Sampling-based Optimization in the Presence of Uncertainty
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Dagstuhl Seminar Proceedings, Volume 9181
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2009-07-30
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Jürgen Branke, Barry L. Nelson, Warren Buckler Powell, and Thomas J. Santner. 09181 Executive Summary – Sampling-based Optimization in the Presence of Uncertainty. In Sampling-based Optimization in the Presence of Uncertainty. Dagstuhl Seminar Proceedings, Volume 9181, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)
https://doi.org/10.4230/DagSemProc.09181.2
Abstract
This Dagstuhl seminar brought together researchers from statistical ranking and selection; experimental design and response-surface modeling; stochastic programming; approximate dynamic programming; optimal learning; and the design and analysis of computer experiments with the goal of attaining a much better mutual understanding of the commonalities and differences of the various approaches to sampling-based optimization, and to take first steps toward an overarching theory, encompassing many of the topics above.
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Keywords
- Optimal learning
- optimization in the presence of uncertainty
- simulation optimization
- sequential experimental design
- ranking and selection
- random search
- stochastic approximation
- approximate dynamic programming
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