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A Class of Polynomial Interior Point Algorithms for the Cartesian P-Matrix Linear Complementarity Problem over Symmetric Cones

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  • G. Q. Wang

    (Shanghai University of Engineering Science)

  • Y. Q. Bai

    (Shanghai University)

Abstract

In this paper, we present a new class of polynomial interior point algorithms for the Cartesian P-matrix linear complementarity problem over symmetric cones based on a parametric kernel function, which determines both search directions and the proximity measure between the iterate and the center path. The symmetrization of the search directions used in this paper is based on the Nesterov and Todd scaling scheme. By using Euclidean Jordan algebras, we derive the iteration bounds that match the currently best known iteration bounds for large- and small-update methods.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:joptap:v:152:y:2012:i:3:d:10.1007_s10957-011-9938-8
    DOI: 10.1007/s10957-011-9938-8
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    References listed on IDEAS

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    1. S. H. Schmieta & F. Alizadeh, 2001. "Associative and Jordan Algebras, and Polynomial Time Interior-Point Algorithms for Symmetric Cones," Mathematics of Operations Research, INFORMS, vol. 26(3), pages 543-564, August.
    2. Peng, Jiming & Roos, Cornelis & Terlaky, Tamas, 2002. "A new class of polynomial primal-dual methods for linear and semidefinite optimization," European Journal of Operational Research, Elsevier, vol. 143(2), pages 234-256, December.
    3. 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.
    4. Akiko Yoshise, 2012. "Complementarity Problems Over Symmetric Cones: A Survey of Recent Developments in Several Aspects," International Series in Operations Research & Management Science, in: Miguel F. Anjos & Jean B. Lasserre (ed.), Handbook on Semidefinite, Conic and Polynomial Optimization, chapter 0, pages 339-375, Springer.
    5. Yu. E. Nesterov & M. J. Todd, 1997. "Self-Scaled Barriers and Interior-Point Methods for Convex Programming," Mathematics of Operations Research, INFORMS, vol. 22(1), pages 1-42, February.
    6. Gu, G. & Zangiabadi, M. & Roos, C., 2011. "Full Nesterov-Todd step infeasible interior-point method for symmetric optimization," European Journal of Operational Research, Elsevier, vol. 214(3), pages 473-484, November.
    7. S. H. Pan & J.-S. Chen, 2009. "Growth Behavior of Two Classes of Merit Functions for Symmetric Cone Complementarity Problems," Journal of Optimization Theory and Applications, Springer, vol. 141(1), pages 167-191, April.
    8. M. Seetharama Gowda & Jong-Shi Pang, 1992. "On Solution Stability of the Linear Complementarity Problem," Mathematics of Operations Research, INFORMS, vol. 17(1), pages 77-83, February.
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    Cited by:

    1. G. Q. Wang & L. C. Kong & J. Y. Tao & G. Lesaja, 2015. "Improved Complexity Analysis of Full Nesterov–Todd Step Feasible Interior-Point Method for Symmetric Optimization," Journal of Optimization Theory and Applications, Springer, vol. 166(2), pages 588-604, August.
    2. Huali Zhao & Hongwei Liu, 2018. "A New Infeasible Mehrotra-Type Predictor–Corrector Algorithm for Nonlinear Complementarity Problems Over Symmetric Cones," Journal of Optimization Theory and Applications, Springer, vol. 176(2), pages 410-427, February.
    3. Li, Yuan-Min & Wei, Deyun, 2015. "A generalized smoothing Newton method for the symmetric cone complementarity problem," Applied Mathematics and Computation, Elsevier, vol. 264(C), pages 335-345.

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