A New Primal–Dual Predictor–Corrector Interior-Point Method for Linear Programming Based on a Wide Neighbourhood

M. Sayadi Shahraki (), H. Mansouri () and M. Zangiabadi ()
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M. Sayadi Shahraki: Shahrekord University
H. Mansouri: Shahrekord University
M. Zangiabadi: Shahrekord University

Journal of Optimization Theory and Applications, 2016, vol. 170, issue 2, No 12, 546-561

Abstract: Abstract In this paper, we propose a new predictor–corrector interior-point algorithm for linear programming based on a wide neighbourhood. In each iteration, the algorithm computes the Ai-Zhang’s predictor direction (SIAM J. Optim. 16(2):400–417, 2005) and a new corrector direction, in an attempt to improve its performance. We drive that the duality gap reduces in both predictor and corrector steps. Moreover, we also prove that the complexity of the algorithm coincides with the best iteration bound for small neighbourhood algorithms. Finally, some numerical experiments are provided which reveal capability and effectiveness of the proposed method.

Keywords: Primal–dual interior-point methods; Predictor–corrector method; Wide neighbourhood; 90C05; 90C51 (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10957-016-0927-9

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