Abstract
In this paper, we present a new primal–dual interior-point algorithm for linear optimization based on a trigonometric kernel function. By simple analysis, we derive the worst case complexity for a large-update primal–dual interior-point method based on this kernel function. This complexity estimate improves a result from El Ghami et al. (2012) and matches the one obtained in Reza Peza Peyghami et al. (2014).
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