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Authors: Chuan Xu ; Jianqiang Cheng and Abdel Lisser

Affiliation: Université Paris-Sud 11, France

Keyword(s): Stochastic Programming, Chance-constrained Programming, Sample Approximation, Semidefinite Program.

Related Ontology Subjects/Areas/Topics: Methodologies and Technologies ; Operational Research ; Stochastic Optimization

Abstract: Semidefinite programming has been widely studied for the last two decades. Semidefinite programs are linear programs with semidefinite constraint generally studied with deterministic data. In this paper, we deal with a stochastic semidefinte programs with chance constraints, which is a generalization of chance-constrained linear programs. Based on existing theoretical results, we develop a new sampling method to solve these chance constraints semidefinite problems. Numerical experiments are conducted to compare our results with the state-of-the-art and to show the strength of the sampling method.

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Paper citation in several formats:
Xu, C.; Cheng, J. and Lisser, A. (2015). A Sampling Method to Chance-constrained Semidefinite Optimization. In Proceedings of the International Conference on Operations Research and Enterprise Systems - ICORES; ISBN 978-989-758-075-8; ISSN 2184-4372, SciTePress, pages 75-81. DOI: 10.5220/0005276400750081

@conference{icores15,
author={Chuan Xu. and Jianqiang Cheng. and Abdel Lisser.},
title={A Sampling Method to Chance-constrained Semidefinite Optimization},
booktitle={Proceedings of the International Conference on Operations Research and Enterprise Systems - ICORES},
year={2015},
pages={75-81},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005276400750081},
isbn={978-989-758-075-8},
issn={2184-4372},
}

TY - CONF

JO - Proceedings of the International Conference on Operations Research and Enterprise Systems - ICORES
TI - A Sampling Method to Chance-constrained Semidefinite Optimization
SN - 978-989-758-075-8
IS - 2184-4372
AU - Xu, C.
AU - Cheng, J.
AU - Lisser, A.
PY - 2015
SP - 75
EP - 81
DO - 10.5220/0005276400750081
PB - SciTePress