Volume 12, Issue 1 (4-2017)                   IJMSI 2017, 12(1): 131-152 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Peyghami M R, Fathi Hafshejani S. An Interior Point Algorithm for Solving Convex Quadratic Semidefinite Optimization Problems Using a New Kernel Function. IJMSI. 2017; 12 (1) :131-152
URL: http://ijmsi.ir/article-1-671-en.html

In this paper, we consider convex quadratic semidefinite optimization problems and provide a primal-dual Interior Point Method (IPM) based on a new kernel function with a trigonometric barrier term. Iteration complexity of the algorithm is analyzed using some easy to check and mild conditions. Although our proposed kernel function is neither a Self-Regular (SR) function nor logarithmic barrier function, the primal-dual IPMs based on this kernel function enjoy the worst case iteration bound $Oleft(sqrt{n}log nlog frac{n}{epsilon}right)$ for the large-update methods with the special choice of its parameters. This bound coincides to the so far best known complexity results obtained from SR kernel functions for linear and semidefinite optimization problems. Finally some numerical issues regarding the practical performance of the new proposed kernel function is reported.

Type of Study: Research paper | Subject: Special

Add your comments about this article : Your username or Email:

© 2018 All Rights Reserved | Iranian Journal of Mathematical Sciences and Informatics

Designed & Developed by : Yektaweb