IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v290y2016icp164-177.html
   My bibliography  Save this article

A partitioned PSB method for partially separable unconstrained optimization problems

Author

Listed:
  • Cao, Huiping
  • Yao, Lan

Abstract

In this paper, we propose a partitioned PSB method for solving partially separable unconstrained optimization problems. By using a projection technique, we construct a sufficient descent direction. Under appropriate conditions, we show that the partitioned PSB method with projected direction is globally and superlinearly convergent for uniformly convex problems. In particular, the unit step length is accepted after finitely many iterations. Finally, some numerical results are presented, which show that the partitioned PSB method is effective and competitive.

Suggested Citation

  • Cao, Huiping & Yao, Lan, 2016. "A partitioned PSB method for partially separable unconstrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 290(C), pages 164-177.
    • Handle: RePEc:eee:apmaco:v:290:y:2016:i:c:p:164-177
    DOI: 10.1016/j.amc.2016.06.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300316303800
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2016.06.009?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. Xiao-Min An & Dong-Hui Li & Yunhai Xiao, 2011. "Sufficient descent directions in unconstrained optimization," Computational Optimization and Applications, Springer, vol. 48(3), pages 515-532, April.
    Full references (including those not matched with items on IDEAS)

    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. XiaoLiang Dong & Hongwei Liu & Yubo He, 2015. "A Self-Adjusting Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition," Journal of Optimization Theory and Applications, Springer, vol. 165(1), pages 225-241, April.
    2. Yunhai Xiao & Chunjie Wu & Soon-Yi Wu, 2015. "Norm descent conjugate gradient methods for solving symmetric nonlinear equations," Journal of Global Optimization, Springer, vol. 62(4), pages 751-762, August.

    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:eee:apmaco:v:290:y:2016:i:c:p:164-177. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.