Abstract
The Primal-dual interior-point methods with a filter are one of hot issues in optimization with both equality and inequality constraints. Extensive attention has been paid and great progress has been made for a long time. Interior-point methods not only have polynomial complexity, but are also highly efficient in practice.
In this paper, we first generalize the dual interior filter algorithm for referred paper. Then a dual interior filter algorithm based on the orthogonal design is proposed. The orthogonal design is used to evenly sample the solution space, then the points with small constraint violations are chosen as the initial points of the algorithm. The dual interior point filter algorithm based on orthogonal design (DIPFA-OD) choosing the initial points is compared with the dual interior point filter algorithm based on random initial points (DIPFA-RND). The experimental results show that DIPFA-OD has a faster convergence speed and obtains better objective values than DIPFA-RND.
Y. Yang and T. Huo—Contributed equally to this work and should be considered co-first authors.
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Acknowledgment
The authors are very grateful to the anonymous reviewers for their constructive comments to this paper. This work is supported by the National Science Foundation of China under Grant 61673355, 61271140 and 61203306.
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Yang, Y., Huo, T., Lan, B., Zeng, S. (2018). A Dual Internal Point Filter Algorithm Based on Orthogonal Design. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_13
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DOI: https://doi.org/10.1007/978-981-13-1651-7_13
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