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Implementation of a primal–dual method for SDP on a shared memory parallel architecture

Brian BorchersContact Information and Joseph G. Young2

(1)  Department of Mathematics, New Mexico Tech, 801 Leroy Place, Socorro, NM 87801, USA
(2)  Computational and Applied Mathematics, Rice University, 6100 Main Street, MS 134, Houston, TX 77005, USA

Received: 27 May 2005  Revised: 7 March 2006  Published online: 27 March 2007

Abstract   Primal–dual interior point methods and the HKM method in particular have been implemented in a number of software packages for semidefinite programming. These methods have performed well in practice on small to medium sized SDPs. However, primal–dual codes have had some trouble in solving larger problems because of the storage requirements and required computational effort. In this paper we describe a parallel implementation of the primal–dual method on a shared memory system. Computational results are presented, including the solution of some large scale problems with over 50,000 constraints.

Keywords  Semidefinite programming - Interior point methods - Parallel computing


Contact Information Brian Borchers
Email: borchers@nmt.edu
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Referenced by
3 newer articles

  1. Gao, Yan (2009) Calibrating Least Squares Semidefinite Programming with Equality and Inequality Constraints. SIAM Journal on Matrix Analysis and Applications 31(3)
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  2. Sivaramakrishnan, Kartik Krishnan (2008) A parallel interior point decomposition algorithm for block angular semidefinite programs. Computational Optimization and Applications
    [CrossRef]
  3. Mitchell, John E. (2007) Selective Gram–Schmidt orthonormalization for conic cutting surface algorithms. Mathematical Methods of Operations Research
    [CrossRef]
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