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A Large-Update Feasible Interior-Point Algorithm for Convex Quadratic Semi-definite Optimization Based on a New Kernel Function
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
).
B. Kheirfam · F. Hasani . A Large-Update Feasible Interior-Point Algorithm for Convex Quadratic Semi-definite Optimization Based on a New Kernel Function[J]. Journal of the Operations Research Society of China, 2013 , 1(3) : 359 -376 . DOI: 10.1007/s40305-013-0023-x
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