Advanced Search
Article Contents
Article Contents

A new smoothing approach to exact penalty functions for inequality constrained optimization problems

Abstract Related Papers Cited by
  • In this study, we introduce a new smoothing approximation to the non-differentiable exact penalty functions for inequality constrained optimization problems. Error estimations are investigated between non-smooth penalty function and smoothed penalty function. In order to demonstrate the effectiveness of proposed smoothing approach the numerical examples are given.
    Mathematics Subject Classification: Primary: 90C30, 57R12; Secondary: 53C35.


    \begin{equation} \\ \end{equation}
  • [1]

    A. M. Bagirov, A. Al Nuamiat and N. Sultanova, Hyperbolic smoothing functions for nonsmooth minimization, Optimization, 62 (2013), 759-782.doi: 10.1080/02331934.2012.675335.


    F. S. Bai, Z. Y. Wu and D. L. Zhu, Lower order calmness and exact penalty fucntion, Optimization Methods ans Software, 21 (2006), 515-525.doi: 10.1080/10556780600627693.


    A. Ben-Tal and M. Teboule, Smoothing technique for nondifferentiable optimization problems, Lecture notes in mathematics, 1405, Springer-Verlag, Heidelberg, (1989), 1-11.


    D. Bertsekas, Nondifferentiable optimization via approximation, Mathematical Programming Study, 3 (1975), 1-25.


    C. Chen and O. L. Mangasarian, A Class of smoothing functions for nonlinear and mixed complementarity problem, Computational Optimization and Application, 5 (1996), 97-138.doi: 10.1007/BF00249052.


    X. Chen, Smoothing Methods for nonsmooth, nonconvex minimzation, Mathematical Programming Serie B, 134 (2012), 71-99.doi: 10.1007/s10107-012-0569-0.


    S. J. Lian, Smoothing approximation to l1 exact penalty for inequality constrained optimization, Applied Mathematics and Computation, 219 (2012), 3113-3121.doi: 10.1016/j.amc.2012.09.042.


    B. Liu, On smoothing exact penalty function for nonlinear constrained optimization problem, Journal of Applied Mathematics and Computing, 30 (2009), 259-270.doi: 10.1007/s12190-008-0171-z.


    M. C. Pinar and S. Zenios, On smoothing exact penalty functions for convex constrained optimization, SIAM Journal on Optimization, 4 (1994), 468-511.doi: 10.1137/0804027.


    Z. Meng, C. Dang, M. Jiang and R. Shen, A smoothing objective penalty function algorithm foe inequality constrained optimization problems, Numerical Functional Analysis and Optimization, 32 (2011), 806-820.doi: 10.1080/01630563.2011.577262.


    Z. Y. Wu, H. W. J. Lee, F. S. Bai and L. S. Zhang, Quadratic smoothing approximation to l1 exact penalty function in global optimization, Journal of Industrail and Management Optimization, 53 (2005), 533-547.doi: 10.3934/jimo.2005.1.533.


    Z. Y. Wu, F. S. Bai, X. Q. Yang and L. S. Zhang, An exact lower orderpenalty function and its smoothing in nonlinear programming, Optimization, 53 (2004), 51-68.doi: 10.1080/02331930410001662199.


    A. E. Xavier, The hyperbolic smoothing clustering method, Pattern Recognition, 43 (2010), 731-737.


    A. E. Xavier,A. A. F. D. Oliveira, Optimal covering of plane domains by circles via hyperbolic smoothing, Journal of Global Optimization, 31 (2005), 493-504.doi: 10.1007/s10898-004-0737-8.


    X. Xu, Z. Meng, J. Sun and R. Shen, A penalty function method based on smoothing lower order penalty function, Journal of Computational and Applied Mathematics, 235 (2011), 4047-4058.doi: 10.1016/j.cam.2011.02.031.


    N. Yilmaz, A. Sahiner, A New Global Optimization Technique Based on the Smoothing Approach for Non-smooth, Non-convex Optimization, Submitted.


    N. Yilmaz, A. Sahiner, Smoothing Approach for Non-lipschitz Optimization, Submitted.


    I. Zang, A smooting out technique for min-max optimization, Mathematical Programming, 19 (1980), 61-77.doi: 10.1007/BF01581628.


    W. I. Zangwill, Nonlinear programing via penalty functions, Management Science, 13 (1967), 344-358.

  • 加载中

Article Metrics

HTML views() PDF downloads(258) Cited by(0)

Access History

Other Articles By Authors



    DownLoad:  Full-Size Img  PowerPoint