A tight iteration-complexity upper bound for the MTY predictor-corrector algorithm via redundant Klee-Minty cubes

It is an open question whether there is an interior-point algorithm for linear optimization problems with a lower iteration-complexity than the classical bound O( √ n log(1 μ0 )). This paper provides a negative answer to that question for a variant of the Mizuno-Todd-Ye predictor-corrector algorithm. In fact, we prove that for any > 0, there is a redundant Klee-Minty cube for which the aforementioned algorithm requires n 1 2− ) iterations to reduce the barrier parameter by at least a constant. This is provably the first case of an adaptive step interior-point algorithm, where the classical iteration-complexity upper bound is shown to be tight.