A new kind of simple kennel function yielding good iteration bounds for primal-dual interior-point methods

Authors:
Liying Liu;Shaoyong Li
Affiliations:
-;-
Venue:
Journal of Computational and Applied Mathematics
Year:
2011

Citing 16
Cited 1

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Abstract

We introduce a new kind of kernel function, which yields efficient large-update primal-dual interior-point methods. We conclude that in some situations its iteration bounds are O(m^3^m^+^1^2^mn^m^+^1^2^mlogn@e), which are at least as good as the best known bounds so far, O(nlognlogn@e), for large-update primal-dual interior-point methods. The result decreases the gap between the practical behavior of the large-update algorithms and their theoretical performance results, which is an open problem. Numerical results show that the algorithms are feasible.