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09181 Executive Summary – Sampling-based Optimization in the Presence of Uncertainty

Authors Jürgen Branke, Barry L. Nelson, Warren Buckler Powell, Thomas J. Santner



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Jürgen Branke
Barry L. Nelson
Warren Buckler Powell
Thomas J. Santner

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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.
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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