AN INTERIOR POINT APPROACH FOR SEMIDEFINITE OPTIMIZATION USING NEW PROXIMITY FUNCTIONS
2009 ◽
Vol 26
(03)
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pp. 365-382
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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 [Formula: see text]. 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 [Formula: see text] iteration bound to solve SDO problems with special choice of the parameters of the new class.
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2016 ◽
Vol 09
(03)
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pp. 1650059
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2010 ◽
Vol 25
(3)
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pp. 387-403
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2004 ◽
Vol 15
(1)
◽
pp. 101-128
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2015 ◽
Vol 93
(3)
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pp. 231-245
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