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Abstract
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In this paper, we propose a predictor-corrector infeasible interior-point algorithm for
semidefinite optimization based on the Nesterov-Todd scaling scheme. In each iteration, the algorithm
computes the new iterate using a new combination of the predictor and corrector directions.
Using the Ai-Zhang’s wide neighborhood for linear complementarity problems, and extended to
semidefinite optimization by Li and Terlaky, it is shown that the iteration complexity bound of
the algorithm is O(n
5
4 log "
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