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Homogeneous Self-dual Algorithms for Stochastic Second-Order Cone Programming

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Jin et al. (in J. Optim. Theory Appl. 155:1073–1083, 2012) proposed homogeneous self-dual algorithms for stochastic semidefinite programs with finite event space. In this paper, we utilize their work to derive homogeneous self-dual algorithms for stochastic second-order cone programs with finite event space. We also show how the structure in the stochastic second-order cone programming problems may be exploited so that the algorithms developed for these problems have less complexity than the algorithms developed for stochastic semidefinite programs mentioned above.

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Acknowledgements

The authors thank the anonymous referees for their valuable suggestions to improve the paper.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, The University of Jordan, P.O. Box 13194, Amman, 11942, Jordan

    Baha Alzalg

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  1. Baha Alzalg
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Correspondence to Baha Alzalg.

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Communicated by René Henrion.

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Alzalg, B. Homogeneous Self-dual Algorithms for Stochastic Second-Order Cone Programming. J Optim Theory Appl 163, 148–164 (2014). https://doi.org/10.1007/s10957-013-0428-z

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  • DOI: https://doi.org/10.1007/s10957-013-0428-z

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