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AN INTERIOR POINT APPROACH FOR SEMIDEFINITE OPTIMIZATION USING NEW PROXIMITY FUNCTIONS.
- Source :
- Asia-Pacific Journal of Operational Research; Jun2009, Vol. 26 Issue 3, p365-382, 18p
- Publication Year :
- 2009
-
Abstract
- Kernel functions play an important role in interior point methods (IPMs) for solving linear optimization (LO) problems to define a new search direction. In this paper, we consider primal-dual algorithms for solving Semidefinite Optimization (SDO) problems based on a new class of kernel functions defined on the positive definite cone S<subscript>++</subscript><superscript>n×n</superscript>. Using some appealing and mild conditions of the new class, we prove with simple analysis that the new class-based large-update primal-dual IPMs enjoy an O(√n log n log n/ϵ) iteration bound to solve SDO problems with special choice of the parameters of the new class. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02175959
- Volume :
- 26
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Asia-Pacific Journal of Operational Research
- Publication Type :
- Academic Journal
- Accession number :
- 44196536
- Full Text :
- https://doi.org/10.1142/S0217595909002250