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Abstract
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In this paper, we present a new primal-dual predictor-corrector interior-point algorithm for linear
optimization problems. In each iteration of this algorithm, we use the new wide neighborhood
proposed by Darvay and Takács. Our algorithm computes the predictor direction, then the
predictor direction is used to obtain the corrector direction. We show that the duality gap reduces
in both predictor and corrector steps. Moreover, we conclude that the complexity bound of this
algorithm coincides with the best-known complexity bound obtained for small neighborhood
algorithms. Eventually, numerical results show the capability and efficiency of the proposed
algorithm.
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