A Primal-Dual Interior-Point Algorithm for Convez Quadratic Optimization Based on A Parametric Kernel Function
In this paper a primal-dual interior-point algorithm for convex quadratic optimization based on a parametric kernel function, with parameters p ? 0,1 and q = 1, is presented. The proposed parametric kernel function is of excellent properties which are used both for determining the search directions and measuring the distance between the given iterate and the central path for the algorithm. These properties enable us to derive the currently best known iteration bound for the algorithm with large-update method, namely, O(vn log n log n/?), which reduces the gap between the practical behavior of the algorithm and its theoretical performance results.
Convez quadratic optimization Interior-point methods Large-update method Iteration bound
Guoqiang Wang Yanqin Bai
College of Advanced Vocational Technology, Shanghai University of Engineering Science, Shanghai 2004 Department of Mathematics, Shanghai University, Shanghai 200444, P.R.China
国际会议
三亚
英文
1798-1802
2009-04-24（万方平台首次上网日期，不代表论文的发表时间）