Abstract
In this work the complexity of a feasible variant of a Linear Programming Predictor-Corrector algorithm specialized for transportation and assignment problems is explored. A \(O(n |\log (\epsilon )|)\) iteration complexity was achieved by proving that the step size computed by the studied algorithm is bounded at each iteration by \(\frac{\theta (4-3\theta )(1-\theta )^2}{n}\), where \(\theta \in [0,1[\). Therefore, allowing to conclude that the analyzed Predictor-Corrector algorithm that uses the 2-norm neighborhood has polynomial iteration complexity and is Q-linearly convergent.
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The research was partially financed by the Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia), within the Projects UIDB/00013/2020 and UIDP/00013/2020.
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Teixeira, A., Almeida, R. (2022). On the Complexity of a Linear Programming Predictor-Corrector Algorithm Using the Two Norm Neighborhood. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds) Optimization, Learning Algorithms and Applications. OL2A 2022. Communications in Computer and Information Science, vol 1754. Springer, Cham. https://doi.org/10.1007/978-3-031-23236-7_37
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