Kernel-function Based Algorithms for Semidefinite Optimization

@article{Ghami2009KernelfunctionBA,
  title={Kernel-function Based Algorithms for Semidefinite Optimization},
  author={Mohamed El Ghami and Yan-Qin Bai and Kees Roos},
  journal={RAIRO - Operations Research},
  year={2009},
  volume={43},
  pages={189-199}
}
Recently, Y.Q. Bai, M. El Ghami and C. Roos [3] introduced a new class of so-called eligible kernel functions which are defined by some simple conditions. The authors designed primal-dual interiorpoint methods for linear optimization (LO) based on eligible kernel functions and simplified the analysis of these methods considerably. In this paper we consider the semidefinite optimization (SDO) problem and we generalize the aforementioned results for LO to SDO. The iteration bounds obtained are… CONTINUE READING

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Self-regularity

  • J. Peng, C. Roos, T. Terlaky
  • a new paradigm for primal-dual interiorpoint…
  • 2002
Highly Influential
7 Excerpts

New primal-dual interior-point methods based on kernel functions

  • M. El Ghami
  • Ph.D. thesis, TU Delft, The Netherlands
  • 2005
1 Excerpt

Aspects of semidefinite programming, Applied Optimization 65

  • E. de Klerk
  • 2002
2 Excerpts

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