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Computational Techniques for Probabilistic Constrained Optimization Problems

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Stochastic Optimization

Part of the book series: Lecture Notes in Economics and Mathematical Systems ((LNE,volume 379))

Abstract

The subject of this paper is to give a survey on algorithms designed for the solution of probabilistic constrained problems with joint probabilistic constraints. A brief summary of the most important convexity results is also included as convexity properties play a central role in developing solution methods for this problem class. For overviews on algorithms aiming the solution of probabilistic constrained problems see also [25], [26] and [50].

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Author notes

  1. János Mayer

    Present address: Computer and Automation Institute, Hungarian Academy of Sciences, Budapest, Hungary

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  1. Institute for Operations Research, University of Zurich, Switzerland

    János Mayer

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  1. János Mayer
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  1. Fakultät für Luft- und Raumfahrttechnik, Universität der Bundeswehr München, Werner-Heisenberg-Weg 39, W-8014, Neubiberg, Germany

    Kurt Marti

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Mayer, J. (1992). Computational Techniques for Probabilistic Constrained Optimization Problems. In: Marti, K. (eds) Stochastic Optimization. Lecture Notes in Economics and Mathematical Systems, vol 379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-88267-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-88267-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-55225-3

  • Online ISBN: 978-3-642-88267-8

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