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A Line Search Penalty-Free Method for Nonlinear Second-Order Cone Programming

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Abstract

In this paper, we propose a line search penalty-free method for solving nonlinear second-order cone programming (NSOCP) problem. Compared with the traditional SQP-type method for NSOCP, our method does not need the assumption that the subproblem is feasible. Besides that, it does not use penalty-function or filter technique. We first use a robust linear second-order cone programming subproblem to get a detective step and then compute an optimal step from a quadratic optimization subproblem. The search direction is a convex combination of the detective step and optimal step. This two-phase strategy and the penalty-free technique are employed to promote global convergence which is analyzed under mild assumptions. We report some numerical experiments whose results show that the proposed algorithm is applicable and efficient.

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References

  1. Alizadeh, F., Goldfarb, D.: Second-order cone programming. Math. Program. 95, 3–51 (2003)

    Article  MathSciNet  Google Scholar 

  2. Alvarez, F., López, J., Ramírez, H.C.: Interior proximal algorithm with variable metric for second-order cone programming: applications to structural optimization and support vector machines. Optim. Methods Softw. 25, 859–881 (2010)

    Article  MathSciNet  Google Scholar 

  3. Asuncion, A., Newman, D.: UCI machine learning repository (2007). Available at http://archive.ics.uci.edu/ml/

  4. Bonnans, J.F., Ramíez, C.H.: Perturbation analysis of second-order cone programming problems. Math. Program. 104, 205–207 (2005)

    Article  MathSciNet  Google Scholar 

  5. Bonnans, J.F., Shapiro, A.: Perturbation Analysis of Optimization Problem. Springer Series in Operations Research. Springer, New York (2000)

    Book  Google Scholar 

  6. Byrd, R.H., Lopez-Calva, G., Nocedal, J.: A line search exact penalty method using steering rules. Math. Program. 133, 39–73 (2012)

    Article  MathSciNet  Google Scholar 

  7. Canelas, A., Carrasco, M., López, J.: A feasible direction algorithm for nonlinear second-order cone programs. Optim. Methods Softw. (2018). https://doi.org/10.1080/10556788.2018.1506452

    Article  MATH  Google Scholar 

  8. Chen, Z.W., Qiu, S.Q.: Global and local convergence of a penalty-free method for nonlinear programming. Comput. Appl. Math. 65, 589–608 (2013)

    Article  MathSciNet  Google Scholar 

  9. Conn, A.R., Gould, N.I.M., Toint Ph, L.: Trust-Region Methods. MOS-SIAM Ser. Optim. SIAM, Philadelphia (2000)

    Book  Google Scholar 

  10. Fletcher, R.: Practical Methods of Optimization, 2nd edn. Wiley, New York (1987)

    MATH  Google Scholar 

  11. Fukuda, E.H., Silva, P.J.S., Fukushima, M.: Differentiable exact penalty functions for nonlinear second-order cone programs. SIAM J. Optim. 22, 1607–1633 (2012)

    Article  MathSciNet  Google Scholar 

  12. Gould, N.I.M., Loh, Y., Robinson, D.P.: A filter method with unified step computation for nonlinear optimization. SIAM J. Optim. 24, 175–209 (2014)

    Article  MathSciNet  Google Scholar 

  13. Hayashi, S., Yamashita, N., Fukushima, M.: A combined smoothing and regularization method for monotone second-order cone complementarity problems. SIAM J. Optim. 15, 593–615 (2005)

    Article  MathSciNet  Google Scholar 

  14. Kanzow, C., Ferenczi, I., Fukushima, M.: On the local convergence of semismooth Newton methods for linear and nonlinear second-order cone programs without strict complementarity. SIAM J. Optim. 20, 297–320 (2009)

    Article  MathSciNet  Google Scholar 

  15. Kato, H., Fukushima, M.: An SQP-type algorithm for nonlinear second-order cone programs. Optim. Lett. 1, 129–144 (2007)

    Article  MathSciNet  Google Scholar 

  16. Lanckriet, G., Ghaoui, L., Bhattacharyya, C., Jordan, M.: A robust minimax approach to classification. J. Mach. Learn. Res. 3, 555–582 (2003)

    MathSciNet  MATH  Google Scholar 

  17. Liu, Y.Z., Zhang, L.W.: Convergence of the augmented Lagrangian method for nonlinear optimization problems over second-order cones. J. Optim. Theory Appl. 139, 557–575 (2008)

    Article  MathSciNet  Google Scholar 

  18. Lobo, M.S., Vandenberghe, L., Boyd, S., Lebret, H.: Applications of second-order cone programming. Linear Algebra Appl. 284, 193–228 (1998)

    Article  MathSciNet  Google Scholar 

  19. Koc̆vara, M., Stingl, M.: PENNON - a code for convex nonlinear and semidefinite programming. Optim. Methods Softw. 18(3), 317–333 (2003)

    Article  MathSciNet  Google Scholar 

  20. Okuno, T., Yasuda, K., Hayashi, S.: S\(l_{1}\)QP based algorithm with trust region technique for solving nonlinear second-order cone programming problems. Interdiscip. Inf. Sci. 21, 97–107 (2015)

    Google Scholar 

  21. Sturm, J.F.: Using SeDuMi 1.02, a Matlab toolbox for optimization over symmetric cones. Optim. Methods Softw. 11, 625–653 (1999)

    Article  MathSciNet  Google Scholar 

  22. Toh, K.C., Tütüncü, R.H., Todd, M.J.: On the implementation and usage of SDPT3-a MATLAB software package for semidefinite-quadratic-linear programming version 4.0, 17 July (2006)

  23. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  24. Wächter, A., Biegler, L.T.: Line search filter methods for nonlinear programming: motivation and global convergence. SIAM J. Comput. 16, 1–31 (2005)

    MathSciNet  MATH  Google Scholar 

  25. Yamashita, H., Yabe, H.: A primal-dual interior point method for nonlinear optimization over second-order cones. Optim. Methods Softw. 24, 407–426 (2009)

    Article  MathSciNet  Google Scholar 

  26. Yang, L., Yu, B., Li, Y.X.: A homotopy method for nonlinear second-order cone programming. Numer. Algorithms 68, 355–365 (2015)

    Article  MathSciNet  Google Scholar 

  27. Zhang, L.W., Gu, J., Xiao, X.T.: A class of nonlinear Lagrangians for nonconvex second order cone programming. Comput. Optim. Appl. 49, 61–99 (2011)

    Article  MathSciNet  Google Scholar 

  28. Zhang, X.S., Liu, Z.H., Liu, S.Y.: A trust region SQP-filter method for nonlinear second-order cone programming. Comput. Math. Appl. 63, 1569–1576 (2012)

    Article  MathSciNet  Google Scholar 

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Acknowledgement

The paper was written when the first author was at the University of Portsmouth as an academic visitor (February 2019–January 2020). The first author wishes to express his sincere thanks to Dr Chee Khian Sim for his advice and help. We would also like to thank the editor and the anonymous referees for their valuable and helpful comments that have improved the quality of this paper greatly.

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Correspondence to Zhongwen Chen.

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The work was supported by Chinese NSF grant 11871362 and Overseas Study Fund and Start-up Fund for doctoral research by JiangSu University of Science and Technology.

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Zhao, Q., Chen, Z. A Line Search Penalty-Free Method for Nonlinear Second-Order Cone Programming. Acta Appl Math 170, 291–317 (2020). https://doi.org/10.1007/s10440-020-00334-w

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