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Kernel-function Based Algorithms for Semidefinite Optimization

Published online by Cambridge University Press:  28 April 2009

M. EL Ghami ,
Y. Q. Bai  and
C. Roos
M. EL Ghami
Affiliation:
Department of Informatics, University of Bergen,Post Box 7803 5020 Bergen, Norway; melghami@ii.uib.no
Y. Q. Bai
Affiliation:
Department of Mathematics, Shanghai University, Shanghai, 200444, P.R. China; yqbai@shu.edu.cn
C. Roos
Affiliation:
Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands; C.Roos@ewi.tudelft.nl
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Abstract

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 interior-point 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 analogous to the results in [3] for LO.

Keywords

Semidefinite optimizationinterior-point methodsprimal-dual methodcomplexity.
Type
Research Article
Information
RAIRO - Operations Research , Volume 43 , Issue 2 , April 2009 , pp. 189 - 199
Copyright
© EDP Sciences, ROADEF, SMAI, 2009

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References

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