Improving a primal–dual simplex-type algorithm using interior point methods

Optimization ◽  
2018 ◽  
Vol 67 (12) ◽  
pp. 2259-2274 ◽  
Author(s):  
T. Glavelis ◽  
N. Ploskas ◽  
N. Samaras
2007 ◽  
Vol 58 (1) ◽  
pp. 69-88 ◽  
Author(s):  
Kazuhiro Kobayashi ◽  
Sunyoung Kim ◽  
Masakazu Kojima

2009 ◽  
Vol 26 (03) ◽  
pp. 365-382 ◽  
Author(s):  
M. REZA PEYGHAMI

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.


1998 ◽  
Vol 67 (222) ◽  
pp. 867-876
Author(s):  
Michael J. Todd ◽  
Zhimin Zhang ◽  
Jet Wimp ◽  
Nico M. Temme

1997 ◽  
Vol 79 (1-3) ◽  
pp. 235-253 ◽  
Author(s):  
Katsuki Fujisawa ◽  
Masakazu Kojima ◽  
Kazuhide Nakata

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