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2010, Volume 6, Issue 2: 363-380. Doi: 10.3934/jimo.2010.6.363
This issue Previous Article Multi-parametric sensitivity analysis of the constraint matrix in piecewise linear fractional programming Next Article Higher-order sensitivity analysis in nonconvex vector optimization

Smoothing Newton algorithm based on a regularized one-parametric class of smoothing functions for generalized complementarity problems over symmetric cones

  • 1.

    Department of Mathematics, School of Science, Tianjin University, Tianjin 300072

Received:  March 31, 2009
Revised:  November 30, 2009
Published:  March 2010
Abstract Related Papers Cited by
    Abstract
  • Based on the KK smoothing function, we introduce a regularized one-parametric class of smoothing functions and show that it is coercive under suitable assumptions. By making use of the introduced regularized one-parametric class of smoothing functions, we investigate a smoothing Newton algorithm for solving the generalized complementarity problems over symmetric cones (GSCCP), where a nonmonotone line search scheme is used. We show that the algorithm is globally and locally superlinearly convergent under suitable assumptions. The theory of Euclidean Jordan algebras is a basic tool in our analysis.
    Mathematics Subject Classification: Primary: 90C22; Secondary: 90C25.

    Citation:
    shu

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