Kernel-function Based Algorithms for Semidefinite Optimization
نویسندگان
چکیده
منابع مشابه
Kernel-function Based Algorithms for Semidefinite Optimization
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) pr...
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We propose a primal-dual interior-point algorithm for semidefinite optimization(SDO) based on a class of kernel functions which are both eligible and self-regular. New search directions and proximity measures are defined based on these functions. We show that the algorithm has O( √ n log ε ) and O( √ n logn log ε ) complexity results for smalland large-update methods, respectively. These are th...
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Interior-point methods (IPMs) for semidefinite optimization (SDO) have been studied intensively, due to their polynomial complexity and practical efficiency. Recently, J.Peng et al. [14, 15] introduced so-called self-regular kernel (and barrier) functions and designed primal-dual interior-point algorithms based on self-regular proximity for linear optimization (LO) problems. They have also exte...
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ژورنال
عنوان ژورنال: RAIRO - Operations Research
سال: 2009
ISSN: 0399-0559,1290-3868
DOI: 10.1051/ro/2009011