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Stochastic Semidefinite Optimization Using Sampling Methods

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Book cover Operations Research and Enterprise Systems (ICORES 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 577))

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Abstract

This paper deals with stochastic semidefinite chance constrained problems. Semidefinite optimization generalizes linear programs, and generally solves deterministic optimization. We propose a new sampling method to solve chance constrained semidefinite optimization problems. Numerical results are given in order to compare the performances of our approach to the state-of-the-art.

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Authors and Affiliations

  1. Laboratoire de Recherche en Informatique, Université Paris-Sud 11, 91405, Orsay Cedex, France

    Chuan Xu, Jianqiang Cheng & Abdel Lisser

Authors
  1. Chuan Xu
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  2. Jianqiang Cheng
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  3. Abdel Lisser
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Corresponding author

Correspondence to Chuan Xu .

Editor information

Editors and Affiliations

  1. EPFL SB-DO, Lausanne, Switzerland

    Dominique de Werra

  2. MAS of INFORMS, Madison, Alabama, USA

    Greg H. Parlier

  3. Complutense University, Madrid, Spain

    Begoña Vitoriano

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Xu, C., Cheng, J., Lisser, A. (2015). Stochastic Semidefinite Optimization Using Sampling Methods. In: de Werra, D., Parlier, G., Vitoriano, B. (eds) Operations Research and Enterprise Systems. ICORES 2015. Communications in Computer and Information Science, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-319-27680-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-27680-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27679-3

  • Online ISBN: 978-3-319-27680-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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