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International Conference on Learning and Intelligent Optimization

LION 2017: Learning and Intelligent Optimization pp 166–179Cite as

Decomposition Descent Method for Limit Optimization Problems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10556))

Abstract

We consider a general limit optimization problem whose goal function need not be smooth in general and only approximation sequences are known instead of exact values of this function. We suggest to apply a two-level approach where approximate solutions of a sequence of mixed variational inequality problems are inserted in the iterative scheme of a selective decomposition descent method. Its convergence is attained under coercivity type conditions.

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Acknowledgement

The results of this work were obtained within the state assignment of the Ministry of Science and Education of Russia, project No. 1.460.2016/1.4. In this work, the author was also supported by Russian Foundation for Basic Research, project No. 16-01-00109 and by grant No. 297689 from Academy of Finland.

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Authors and Affiliations

  1. Institute of Computational Mathematics and Information Technologies, Kazan Federal University, Kremlevskaya St. 18, 420008, Kazan, Russia

    Igor Konnov

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  1. Igor Konnov
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Correspondence to Igor Konnov .

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Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Calabria, Rende, Italy

    Dmitri E. Kvasov

  3. University of Calabria, Rende, Italy

    Yaroslav D. Sergeyev

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Konnov, I. (2017). Decomposition Descent Method for Limit Optimization Problems. In: Battiti, R., Kvasov, D., Sergeyev, Y. (eds) Learning and Intelligent Optimization. LION 2017. Lecture Notes in Computer Science(), vol 10556. Springer, Cham. https://doi.org/10.1007/978-3-319-69404-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-69404-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69403-0

  • Online ISBN: 978-3-319-69404-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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