A large-update interior-point algorithm for convex quadratic semi-definite optimization based on a new kernel function

作者：Zhang, Ming Wang^*

来源：Acta Mathematica Sinica-English Series, 2012, 28(11): 2313-2328.

DOI：10.1007/s10114-012-0194-0

• Convex quadratic semi-definite optimization
• kernel function
• interior-point algorithm
• large-update method
• complexity

In this paper, we present a large-update interior-point algorithm for convex quadratic semi-definite optimization based on a new kernel function. The proposed function is strongly convex. It is not self-regular function and also the usual logarithmic function. The goal of this paper is to investigate such a kernel function and show that the algorithm has favorable complexity bound in terms of the elegant analytic properties of the kernel function. The complexity bound is shown to be . This bound is better than that by the classical primal-dual interior-point methods based on logarithmic barrier function and recent kernel functions introduced by some authors in optimization fields. Some computational results have been provided.

• 出版日期2012-11
• 单位三峡大学