Kernel-function Based Algorithms for Semidefinite Optimization
Department of Informatics, University of Bergen,Post Box 7803 5020 Bergen, Norway;
2 Department of Mathematics, Shanghai University, Shanghai, 200444, P.R. China; firstname.lastname@example.org
3 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
Accepted: 27 January 2009
Recently, Y.Q. Bai, M. El Ghami and C. Roos  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  for LO.
Mathematics Subject Classification: 90C22 / 90C31
Key words: Semidefinite optimization / interior-point methods / primal-dual method / complexity.
© EDP Sciences, ROADEF, SMAI, 2009