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Abstract
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In this paper, we present a new second-order predictor-corrector interior-point method
for semidefinite optimization. The algorithm is based on the wide neighborhood of the central
path and modified corrector directions. In the corrector step, we derive the step size and corrector
directions which guarantee that new iterate lies in the wide neighborhood. The iteration complexity
bound is O(pn log X0S0
) for the Nesterov-Todd direction, which coincides with the best
known complexity results for semidefinite optimization. Some numerical results are provided as
well.
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