In this paper we present a large-update primal-dual interior-point algorithm for convex quadratic semi-definite optimization problems based on a new parametric kernel function.The goal of this paper is to investigate ...In this paper we present a large-update primal-dual interior-point algorithm for convex quadratic semi-definite optimization problems based on a new parametric kernel function.The goal of this paper is to investigate such a kernel function and show that the algorithm has the best complexity bound.The complexity bound is shown to be O(√n log n log n/∈).展开更多
基金The authors gratefully acknowledge the help of the editor and anonymous referees in improving the readability of the paper.
文摘In this paper we present a large-update primal-dual interior-point algorithm for convex quadratic semi-definite optimization problems based on a new parametric kernel function.The goal of this paper is to investigate such a kernel function and show that the algorithm has the best complexity bound.The complexity bound is shown to be O(√n log n log n/∈).
