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On Primal—Dual Path—Following Algorithms for Semidefinite Programming

  • E. de Klerk
  • C. Roos
  • T. Terlaky
Part of the Applied Optimization book series (APOP, volume 13)

Abstract

Interior point methods for semidefinite programming have recently been studied intensively, due to their polynomial complexity and practical efficiency. Most of these methods are extensions of linear programming algorithms. The primal-dual central path following method for linear programming by Jansen et al. [6] has recently been extended to semidefinite programming by Jiang [7], utilizing the Nesterov-Todd direction and introducing a new distance measure. In this note we refine and extend this analysis: A weaker condition for a feasible full Newton step is established, and quadratic convergence to target points on the central path is shown. Moreover, we show how to compute large dynamic target updates which still allow full Newton steps.

Keywords

Interior Point Method Central Path Semidefinite Program Quadratic Convergence Superlinear Convergence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • E. de Klerk
    • 1
  • C. Roos
    • 1
  • T. Terlaky
    • 1
  1. 1.Faculty of Technical Mathematics and InformaticsDelft University of TechnologyDelftThe Netherlands

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