License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.09181.2
URN: urn:nbn:de:0030-drops-21161
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2009/2116/
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Branke, Jürgen ; Nelson, Barry L. ; Powell, Warren Buckler ; Santner, Thomas J.

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

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09181.SWM.ExtAbstr.2116.pdf (0.06 MB)

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.

BibTeX - Entry

@InProceedings{branke_et_al:DagSemProc.09181.2,
  author =	{Branke, J\"{u}rgen and Nelson, Barry L. and Powell, Warren Buckler and Santner, Thomas J.},
  title =	{{09181 Executive Summary – Sampling-based Optimization in the Presence of Uncertainty }},
  booktitle =	{Sampling-based Optimization in the Presence of Uncertainty},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9181},
  editor =	{J\"{u}rgen Branke and Barry L. Nelson and Warren Buckler Powell and Thomas J. Santner},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2009/2116},
  URN =		{urn:nbn:de:0030-drops-21161},
  doi =		{10.4230/DagSemProc.09181.2},
  annote =	{Keywords: Optimal learning, optimization in the presence of uncertainty, simulation optimization, sequential experimental design, ranking and selection, random search, stochastic approximation, approximate dynamic programming}
}

Keywords: Optimal learning, optimization in the presence of uncertainty, simulation optimization, sequential experimental design, ranking and selection, random
Freie Schlagwörter (englisch): search, stochastic approximation, approximate dynamic programming
Collection: 09181 - Sampling-based Optimization in the Presence of Uncertainty
Issue Date: 2009
Date of publication: 30.07.2009

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