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.
@InProceedings{branke_et_al:DagSemProc.09181.1, author = {Branke, J\"{u}rgen and Nelson, Barry L. and Powell, Warren Buckler and Santner, Thomas J.}, title = {{09181 Abstracts Collection – Sampling-based Optimization in the Presence of Uncertainty}}, booktitle = {Sampling-based Optimization in the Presence of Uncertainty}, pages = {1--15}, 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/entities/document/10.4230/DagSemProc.09181.1}, URN = {urn:nbn:de:0030-drops-21187}, doi = {10.4230/DagSemProc.09181.1}, 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} }
Feedback for Dagstuhl Publishing