Kernel-function based algorithms for semidefinite optimization

Kernel-function based algorithms for semidefinite optimization

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Article ID: iaor200971029
Country: France
Volume: 43
Issue: 2
Start Page Number: 189
End Page Number: 199
Publication Date: Apr 2009
Journal: RAIRO Operations Research
Authors: , ,
Keywords: programming (semidefinite)
Abstract:

Recently, the authors 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 the earlier paper for LO.

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