Primal-Dual Interior-Point Methods for Second-Order Cone Complementarity Based on a New Class of Kernel Function

2010 ◽  
Author(s):  
Xue-mei Yang ◽  
Hua-li Zhao ◽  
Guo-ling Hu
Keyword(s):  
Kernel Function ◽  
Interior Point ◽  
Interior Point Methods ◽  
Second Order ◽  
New Class ◽  
Second Order Cone ◽  
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.

scholarly journals New complexity analysis of full Nesterov-Todd step infeasible interior point method for second-order cone optimization

2018 ◽  
Vol 28 (1) ◽  
pp. 21-38
Author(s):  
Behrouz Kheirfam
Keyword(s):  
Interior Point ◽  
Interior Point Method ◽  
Interior Point Methods ◽  
Complexity Analysis ◽  
Second Order ◽  
Interior Point Algorithm ◽  
Complexity Bound ◽  
Second Order Cone ◽  
Infeasible Interior Point Method ◽  
Barrier Parameter

We present a full Nesterov-Todd (NT) step infeasible interior-point algorithm for second-order cone optimization based on a different way to calculate feasibility direction. In each iteration of the algorithm we use the largest possible barrier parameter value ?. Moreover, each main iteration of the algorithm consists of a feasibility step and a few centering steps. The feasibility step differs from the feasibility step of the other existing methods. We derive the complexity bound which coincides with the best known bound for infeasible interior point methods.

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