Title :
A New Primal-Dual Interior-Point Algorithm for Convex Quadratic Symmetric Cone Optimization Based on a Parametric Kernel Function
Author :
Wang, Guoqiang ; Wang, Fayan
Author_Institution :
Coll. of Fundamental Studies, Shanghai Univ. of Eng. Sci., Shanghai, China
Abstract :
In this paper, we present a class of primal-dual interior-point algorithms for convex quadratic symmetric cone optimization based on a parametric kernel function, which has been introduced for linear optimization. By using Euclidean Jordan algebras, we derive the iteration bounds that match the currently best known iteration bounds for large- and small-update methods, which are as good as the linear optimization analogue.
Keywords :
algebra; convex programming; linear programming; quadratic programming; Euclidean Jordan algebras; convex quadratic symmetric cone optimization; iteration bounds; linear optimization; parametric kernel function; primal-dual interior-point algorithm; Algebra; Algorithm design and analysis; Educational institutions; Kernel; Optimization; Polynomials; Convex quadratic symmetric cone optimization; Euclidean Jordan algebra; Interior-point methods; Kernel function; Large- and small-update methods;
Conference_Titel :
Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4673-1365-0
DOI :
10.1109/CSO.2012.52
