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Structured Primal-dual Interior-point Methods for Banded Semidefinite Programming

  • Zhiming Deng
  • Ming Gu
  • Michael L. Overton
Part of the Operator Theory: Advances and Applications book series (OT, volume 202)

Abstract

For semidefinite programming (SDP) problems, traditional primal-dual interior-point methods based on conventional matrix operations have an upper limit on the problem size that the computer can handle due to memory constraints. But for a special kind of SDP problem, which is called the banded symmetric semidefinite programming (BSDP) problem, a memory-efficient algorithm, called a structured primal-dual interior-point method, can be applied. The method is based on the observation that both banded matrices and their inverses can be represented in sequentially semi-separable (SSS) form with numerical ranks equal to the half bandwidths of the banded matrices. Moreover, all computation can be done sequentially using the SSS form. Experiments of various problem sizes are performed to verify the feasibility of the proposed method.

Keywords

Banded matrix semidefinite program interior-point method sequentially semi-separable 

Mathematics Subject Classification (2000)

65F05 90C22 90C51 65F99 90C25 

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Copyright information

© Birkhäuser Verlag Basel/Switzerland 2010

Authors and Affiliations

  • Zhiming Deng
    • 1
  • Ming Gu
    • 2
  • Michael L. Overton
    • 3
  1. 1.Berkeley Wireless Research CenterBerkeleyUSA
  2. 2.Department of MathematicsUniversity of California at BerkeleyBerkeleyUSA
  3. 3.Courant Institute of Mathematical SciencesNew York UniversityNew YorkUSA

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