IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/1389.html
   My bibliography  Save this paper

On the long step path--following method for semidefinite programming

Author

Listed:
  • Sturm, J.F.
  • Zhang, S.

Abstract

It has been shown in various recent research reports that the analysis of short step primal-dual path following algorithms for linear programming can be nicely generalized to semidefinite programming. However, the analysis of long step path-following algorithms for semidefinite programming appeared to be less straightforward. For such an algorithm, Monteiro obtained an [TeX: O(n^1.5 log(1/ epsilon))] iteration bound for obtaining an epsilon-optimal solution, where n is the order of the semidefinite decision variable. In this paper, we propose to use a different search direction, viz. the so-called V-space direction. It is shown that this modification reduces the iteration complexity to [TeX: O(n log(1/ epsilon))]. Independently, Monteiro and Y. Zhang obtained a similar result using Nesterov-Todd directions.

Suggested Citation

  • Sturm, J.F. & Zhang, S., 1996. "On the long step path--following method for semidefinite programming," Econometric Institute Research Papers EI 9638-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1389
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:ems:eureir:1389. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    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.