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Abstract
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In this paper, we propose a new predictor-corrector interior-point algorithm for
semidefinite optimization based on a wide neighborhood of the central path. We
show that, in addition to the predictor step, each corrector step decreases the duality
gap as well. We also prove that the iteration complexity of the proposed algorithm
coincides with the best iteration bound for small neighborhood algorithms that use
the Nesterov-Todd direction. Finally, some numerical results are provided as well.
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