NUMERICAL RESULTS OF PRIMAL-DUAL INTERIOR-POINT ALGORITHMS FOR SEMIDEFINITE OPTIMIZATION BASED ON KERNEL FUNCTIONS

2015 ◽  
Vol 93 (3) ◽  
pp. 231-245
Author(s):  
Adama Coulibaly ◽  
David Paul Tuo ◽  
Assohoun Adje
Keyword(s):  
Interior Point ◽  
Kernel Functions ◽  
Numerical Results ◽  
Semidefinite Optimization ◽  
Interior Point Algorithms ◽  
Primal Dual

AN INTERIOR POINT APPROACH FOR SEMIDEFINITE OPTIMIZATION USING NEW PROXIMITY FUNCTIONS

2009 ◽  
Vol 26 (03) ◽  
pp. 365-382 ◽  
Author(s):  
M. REZA PEYGHAMI
Keyword(s):  
Interior Point ◽  
Interior Point Methods ◽  
Linear Optimization ◽  
Kernel Functions ◽  
Semidefinite Optimization ◽  
Special Choice ◽  
Simple Analysis ◽  
New Class ◽  
Iteration Bound ◽  
Primal Dual

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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