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Numerical Techniques in Relaxed Optimization Problems

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Book cover Robust Optimization-Directed Design

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 81))

Summary

Young measures and their various generalizations are a basic analytical tool for extension (=relaxation) of optimization problems that may lack of solutions because of various oscillation/concentration effects. Various numerical techniques designed directly for the relaxed problems are surveyed together with some specific applications.

This research was partly covered by the grants IAA 1075402 (GA AV ČR), and MSM 0021620839 (MŠMT ČR). Special thanks are to dr. Martin Kružík for his comments and for the calculations that have been exploited for Figures 8.3 and 8.4.

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Author information

Authors and Affiliations

  1. Mathematical Institute, Charles University, Sokolovská 83, CZ-186 75, Praha 8, Czech Republic

    Tomáš Roubíček

  2. Institute of Information Theory and Automation, Academy of Sciences, Pod vodárenskou věží 4, CZ-182 08, Praha 8, Czech Republic

    Tomáš Roubíček

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  1. Tomáš Roubíček
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Editors and Affiliations

  1. University of Florida, Gainesville, Florida

    Andrew J. Kurdila  & Panos M. Pardalos  & 

  2. Stevens Institute of Technology, Hoboken, New Jersey

    Michael Zabarankin

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Roubíček, T. (2006). Numerical Techniques in Relaxed Optimization Problems. In: Kurdila, A.J., Pardalos, P.M., Zabarankin, M. (eds) Robust Optimization-Directed Design. Nonconvex Optimization and Its Applications, vol 81. Springer, Boston, MA. https://doi.org/10.1007/0-387-28654-3_8

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