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A new class of polynomial primal-dual methods for linear and semidefinite optimization

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  • Peng, Jiming
  • Roos, Cornelis
  • Terlaky, Tamas

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  • 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.
    • Handle: RePEc:eee:ejores:v:143:y:2002:i:2:p:234-256
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    1. 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.
    2. J. Peng & C. Roos & T. Terlaky, 2000. "New Complexity Analysis of the Primal—Dual Newton Method for Linear Optimization," Annals of Operations Research, Springer, vol. 99(1), pages 23-39, December.
    3. Andersen, E.D. & Gondzio, J. & Meszaros, C. & Xu, X., 1996. "Implementation of Interior Point Methods for Large Scale Linear Programming," Papers 96.3, Ecole des Hautes Etudes Commerciales, Universite de Geneve-.
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    Cited by:

    1. 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.
    2. Petra Renáta Rigó & Zsolt Darvay, 2018. "Infeasible interior-point method for symmetric optimization using a positive-asymptotic barrier," Computational Optimization and Applications, Springer, vol. 71(2), pages 483-508, November.
    3. 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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