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A Wide Neighborhood Interior-Point Algorithm for Convex Quadratic Semidefinite Optimization

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Abstract

In this paper, we propose an interior-point algorithm based on a wide neighborhood for convex quadratic semidefinite optimization problems. Using the Nesterov–Todd direction as the search direction, we prove the convergence analysis and obtain the polynomial complexity bound of the proposed algorithm. Although the algorithm belongs to the class of large-step interior-point algorithms, its complexity coincides with the best iteration bound for short-step interior-point algorithms. The algorithm is also implemented to demonstrate that it is efficient.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, Imam Hossein Comprehensive University, Tehran, Iran

    Mohammad Pirhaji, Ali Nakhaei & Ali Shojaeifard

  2. Department of Applied Mathematics, Shahrekord University, Shahrekord, Iran

    Maryam Zangiabadi & Hossien Mansouri

Authors
  1. Mohammad Pirhaji
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  2. Maryam Zangiabadi
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  3. Hossien Mansouri
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  4. Ali Nakhaei
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  5. Ali Shojaeifard
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Correspondence to Mohammad Pirhaji.

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Pirhaji, M., Zangiabadi, M., Mansouri, H. et al. A Wide Neighborhood Interior-Point Algorithm for Convex Quadratic Semidefinite Optimization. J. Oper. Res. Soc. China 8, 145–164 (2020). https://doi.org/10.1007/s40305-018-0219-1

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  • DOI: https://doi.org/10.1007/s40305-018-0219-1

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