Stochastic Algorithms: Foundations and Applications: International Symposium, SAGA 2001 Berlin, Germany, December 13-14, 2001 Proceedings (Lecture Notes in Computer Science, 2264)

ISBN 10: 3540430253 / ISBN 13: 9783540430254

Published by Springer, 2001

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Bibliographic Details

• Title: Stochastic Algorithms: Foundations and Applications: International Symposium, SAGA 2001 Berlin, Germany, December 13-14, 2001 Proceedings (Lecture Notes in Computer Science, 2264)
• Publisher: Springer
• Publication Date: 2001
• Language: English
• ISBN 10: 3540430253
• ISBN 13: 9783540430254
• Binding: Soft cover
• Condition: New

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Synopsis

SAGA 2001, the ?rst Symposium on Stochastic Algorithms, Foundations and Applications, took place on December 13–14, 2001 in Berlin, Germany. The present volume comprises contributed papers and four invited talks that were included in the ?nal program of the symposium. Stochastic algorithms constitute a general approach to ?nding approximate solutions to a wide variety of problems. Although there is no formal proof that stochastic algorithms perform better than deterministic ones, there is evidence by empirical observations that stochastic algorithms produce for a broad range of applications near-optimal solutions in a reasonable run-time. The symposium aims to provide a forum for presentation of original research in the design and analysis, experimental evaluation, and real-world application of stochastic algorithms. It focuses, in particular, on new algorithmic ideas invo- ing stochastic decisions and exploiting probabilistic properties of the underlying problem domain. The program of the symposium re?ects the e?ort to promote cooperation among practitioners and theoreticians and among algorithmic and complexity researchers of the ?eld. In this context, we would like to express our special gratitude to DaimlerChrysler AG for supporting SAGA 2001. The contributed papers included in the proceedings present results in the following areas: Network and distributed algorithms; local search methods for combinatorial optimization with application to constraint satisfaction problems, manufacturing systems, motor control unit calibration, and packing ?exible - jects; and computational learning theory.