IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v75y2020i3d10.1007_s10589-019-00141-6.html
   My bibliography  Save this article

Saddle points of rational functions

Author

Listed:
  • Guangming Zhou

    (Xiangtan University)

  • Qin Wang

    (Xiangtan University)

  • Wenjie Zhao

    (Xiangtan University)

Abstract

This paper concerns saddle points of rational functions, under general constraints. Based on optimality conditions, we propose an algorithm for computing saddle points. It uses Lasserre’s hierarchy of semidefinite relaxation. The algorithm can get a saddle point if it exists, or it can detect its nonexistence if it does not. Numerical experiments show that the algorithm is efficient for computing saddle points of rational functions.

Suggested Citation

  • Guangming Zhou & Qin Wang & Wenjie Zhao, 2020. "Saddle points of rational functions," Computational Optimization and Applications, Springer, vol. 75(3), pages 817-832, April.
    • Handle: RePEc:spr:coopap:v:75:y:2020:i:3:d:10.1007_s10589-019-00141-6
    DOI: 10.1007/s10589-019-00141-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10589-019-00141-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10589-019-00141-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jibetean, D. & de Klerk, E., 2006. "Global optimization of rational functions : A semidefinite programming approach," Other publications TiSEM 25febbc3-cd0c-4eb7-9d37-d, Tilburg University, School of Economics and Management.
    2. Bruce Cox & Anatoli Juditsky & Arkadi Nemirovski, 2017. "Decomposition Techniques for Bilinear Saddle Point Problems and Variational Inequalities with Affine Monotone Operators," Journal of Optimization Theory and Applications, Springer, vol. 172(2), pages 402-435, February.
    3. Feng Guo & Li Wang & Guangming Zhou, 2014. "Minimizing rational functions by exact Jacobian SDP relaxation applicable to finite singularities," Journal of Global Optimization, Springer, vol. 58(2), pages 261-284, February.
    4. A. Nedić & A. Ozdaglar, 2009. "Subgradient Methods for Saddle-Point Problems," Journal of Optimization Theory and Applications, Springer, vol. 142(1), pages 205-228, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wenjie Zhao & Guangming Zhou, 2022. "Local saddle points for unconstrained polynomial optimization," Computational Optimization and Applications, Springer, vol. 82(1), pages 89-106, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wenjie Zhao & Guangming Zhou, 2022. "Local saddle points for unconstrained polynomial optimization," Computational Optimization and Applications, Springer, vol. 82(1), pages 89-106, May.
    2. Vinayaka G. Yaji & Shalabh Bhatnagar, 2020. "Stochastic Recursive Inclusions in Two Timescales with Nonadditive Iterate-Dependent Markov Noise," Mathematics of Operations Research, INFORMS, vol. 45(4), pages 1405-1444, November.
    3. Liusheng Hou & Hongjin He & Junfeng Yang, 2016. "A partially parallel splitting method for multiple-block separable convex programming with applications to robust PCA," Computational Optimization and Applications, Springer, vol. 63(1), pages 273-303, January.
    4. Feng Guo & Li Wang & Guangming Zhou, 2014. "Minimizing rational functions by exact Jacobian SDP relaxation applicable to finite singularities," Journal of Global Optimization, Springer, vol. 58(2), pages 261-284, February.
    5. Desmond Cai & Subhonmesh Bose & Adam Wierman, 2019. "On the Role of a Market Maker in Networked Cournot Competition," Mathematics of Operations Research, INFORMS, vol. 44(3), pages 1122-1144, August.
    6. Denizalp Goktas & Amy Greenwald, 2022. "Gradient Descent Ascent in Min-Max Stackelberg Games," Papers 2208.09690, arXiv.org.
    7. Regina S. Burachik & Alfredo N. Iusem & Jefferson G. Melo, 2013. "An Inexact Modified Subgradient Algorithm for Primal-Dual Problems via Augmented Lagrangians," Journal of Optimization Theory and Applications, Springer, vol. 157(1), pages 108-131, April.
    8. Jae Hyoung Lee & Liguo Jiao, 2018. "Solving Fractional Multicriteria Optimization Problems with Sum of Squares Convex Polynomial Data," Journal of Optimization Theory and Applications, Springer, vol. 176(2), pages 428-455, February.
    9. Samira Mir Mazhari Anvar & Sohrab Khanmohammadi & Javad Musevi niya, 2016. "Game theoretic power allocation for fading MIMO multiple access channels with imperfect CSIR," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 61(4), pages 875-886, April.
    10. Etienne de Klerk & Jean B. Lasserre & Monique Laurent & Zhao Sun, 2017. "Bound-Constrained Polynomial Optimization Using Only Elementary Calculations," Mathematics of Operations Research, INFORMS, vol. 42(3), pages 834-853, August.
    11. Avinash N. Madavan & Subhonmesh Bose, 2021. "A Stochastic Primal-Dual Method for Optimization with Conditional Value at Risk Constraints," Journal of Optimization Theory and Applications, Springer, vol. 190(2), pages 428-460, August.
    12. Philipp Renner & Karl Schmedders, 2017. "Dynamic Principal–Agent Models," Working Papers 203620456, Lancaster University Management School, Economics Department.
    13. Eleftherios Couzoudis & Philipp Renner, 2013. "Computing generalized Nash equilibria by polynomial programming," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 77(3), pages 459-472, June.
    14. Guigues, Vincent & Shapiro, Alexander & Cheng, Yi, 2023. "Duality and sensitivity analysis of multistage linear stochastic programs," European Journal of Operational Research, Elsevier, vol. 308(2), pages 752-767.
    15. Andrea Simonetto & Hadi Jamali-Rad, 2016. "Primal Recovery from Consensus-Based Dual Decomposition for Distributed Convex Optimization," Journal of Optimization Theory and Applications, Springer, vol. 168(1), pages 172-197, January.
    16. Bo Wei & William B. Haskell & Sixiang Zhao, 2020. "An inexact primal-dual algorithm for semi-infinite programming," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 91(3), pages 501-544, June.
    17. Hu, Mian & Wang, Yan-Wu & Xiao, Jiang-Wen & Lin, Xiangning, 2019. "Multi-energy management with hierarchical distributed multi-scale strategy for pelagic islanded microgrid clusters," Energy, Elsevier, vol. 185(C), pages 910-921.
    18. Yaohua Hu & Jiawen Li & Carisa Kwok Wai Yu, 2020. "Convergence rates of subgradient methods for quasi-convex optimization problems," Computational Optimization and Applications, Springer, vol. 77(1), pages 183-212, September.
    19. Philipp Renner & Karl Schmedders, 2020. "Discrete‐time dynamic principal–agent models: Contraction mapping theorem and computational treatment," Quantitative Economics, Econometric Society, vol. 11(4), pages 1215-1251, November.
    20. A. M. Bagirov & L. Jin & N. Karmitsa & A. Al Nuaimat & N. Sultanova, 2013. "Subgradient Method for Nonconvex Nonsmooth Optimization," Journal of Optimization Theory and Applications, Springer, vol. 157(2), pages 416-435, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:coopap:v:75:y:2020:i:3:d:10.1007_s10589-019-00141-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.