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A Class of Large-Update and Small-Update Primal-Dual Interior-Point Algorithms for Linear Optimization

Author

Listed:
  • Y. Q. Bai

    (Shanghai University)

  • G. Lesaja

    (Georgia Southern University)

  • C. Roos

    (Delft University of Technology)

  • G. Q. Wang

    (Shanghai University)

  • M. El Ghami

    (University of Bergen)

Abstract

In this paper we present a class of polynomial primal-dual interior-point algorithms for linear optimization based on a new class of kernel functions. This class is fairly general and includes the classical logarithmic function, the prototype self-regular function, and non-self-regular kernel functions as special cases. The analysis of the algorithms in the paper follows the same line of arguments as in Bai et al. (SIAM J. Optim. 15:101–128, [2004]), where a variety of non-self-regular kernel functions were considered including the ones with linear and quadratic growth terms. However, the important case when the growth term is between linear and quadratic was not considered. The goal of this paper is to introduce such class of kernel functions and to show that the interior-point methods based on these functions have favorable complexity results. They match the currently best known iteration bounds for the prototype self-regular function with quadratic growth term, the simple non-self-regular function with linear growth term, and the classical logarithmic kernel function. In order to achieve these complexity results, several new arguments had to be used.

Suggested Citation

  • Y. Q. Bai & G. Lesaja & C. Roos & G. Q. Wang & M. El Ghami, 2008. "A Class of Large-Update and Small-Update Primal-Dual Interior-Point Algorithms for Linear Optimization," Journal of Optimization Theory and Applications, Springer, vol. 138(3), pages 341-359, September.
    • Handle: RePEc:spr:joptap:v:138:y:2008:i:3:d:10.1007_s10957-008-9389-z
    DOI: 10.1007/s10957-008-9389-z
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    Citations

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    Cited by:

    1. Marianna E.-Nagy & Anita Varga, 2023. "A new long-step interior point algorithm for linear programming based on the algebraic equivalent transformation," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(3), pages 691-711, September.
    2. G. Q. Wang & Y. Q. Bai, 2012. "A Class of Polynomial Interior Point Algorithms for the Cartesian P-Matrix Linear Complementarity Problem over Symmetric Cones," Journal of Optimization Theory and Applications, Springer, vol. 152(3), pages 739-772, March.
    3. Zsolt Darvay & Petra Renáta Takács, 2018. "Large-step interior-point algorithm for linear optimization based on a new wide neighbourhood," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 551-563, September.
    4. Jingyong Tang & Jinchuan Zhou, 2021. "Quadratic convergence analysis of a nonmonotone Levenberg–Marquardt type method for the weighted nonlinear complementarity problem," Computational Optimization and Applications, Springer, vol. 80(1), pages 213-244, September.
    5. G. Lesaja & C. Roos, 2011. "Kernel-Based Interior-Point Methods for Monotone Linear Complementarity Problems over Symmetric Cones," Journal of Optimization Theory and Applications, Springer, vol. 150(3), pages 444-474, September.
    6. Manuel V. C. Vieira, 2012. "The Accuracy of Interior-Point Methods Based on Kernel Functions," Journal of Optimization Theory and Applications, Springer, vol. 155(2), pages 637-649, November.

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