Volume no :3, Issue no: 2, December (2015)

International Journal of Applied Mathematics and Machine Learning

ITERATION BOUNDS OF INTERIOR-POINT METHODS FOR LINEAR OPTIMIZATION BASED ON A NEW KERNEL FUNCTION

Author's: Liying Liu, Tao Hua and Bo Sun
Pages: [89] - [111]
Received Date: September 4, 2014
Submitted by: Jianqiang Gao.
DOI: http://dx.doi.org/10.18642/ijamml_7100121393

Abstract

In this paper, a new kernel function, which yields an efficient primal-dual interior-point method, is introduced. Iteration bounds for large-update methods are derived, namely, which significantly improve the classical iteration bounds. Iteration bounds for small-update methods are derived, namely, which are currently the best known bounds.

Keywords

linear optimization, primal-dual interior-point methods, large-update methods, small-update methods, kernel function.