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Generalized Project Gradient Algorithm for Solving Constrained Minimax Problems

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Bio-inspired Computing – Theories and Applications (BIC-TA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 682))

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Abstract

In this work, constrained minimax problems are studied. By use of proposing a differentially auxiliary function and providing explicit search direction with the aid idea of generalized gradient project technique, a new algorithm with Armjio non-exact linear search is presented and its global convergence is obtained under arbitrary initial point condition.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation (11361018), the Natural Science Foundation of Guangxi Province (2014GXN SFFA118001), the Key Program for Science and Technology in Henan Education Institution (15B110008, 17A110030) and Huarui College Science Foundation (2014qn35) of China.

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Authors and Affiliations

  1. School of Mathematics and Information, Xinyang University, Xinyang, 464000, People’s Republic of China

    Cong Zhang & Limin Sun

  2. College of Mathematics and Computational Science, Guilin University of Electronic Technology, Guilin, 541004, People’s Republic of China

    Zhibin Zhu

Authors
  1. Cong Zhang
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  2. Limin Sun
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  3. Zhibin Zhu
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Corresponding author

Correspondence to Cong Zhang .

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Editors and Affiliations

  1. Xidian University , Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Southwest Jiaotong University , Chengdu, China

    Gexiang Zhang

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© 2016 Springer Nature Singapore Pte Ltd.

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Zhang, C., Sun, L., Zhu, Z. (2016). Generalized Project Gradient Algorithm for Solving Constrained Minimax Problems. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_17

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  • DOI: https://doi.org/10.1007/978-981-10-3614-9_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3613-2

  • Online ISBN: 978-981-10-3614-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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