Skip to main content
Log in

A BFGS trust-region method for nonlinear equations

  • Published:
Computing Aims and scope Submit manuscript

Abstract

In this paper, a new trust-region subproblem combining with the BFGS update is proposed for solving nonlinear equations, where the trust region radius is defined by a new way. The global convergence without the nondegeneracy assumption and the quadratic convergence are obtained under suitable conditions. Numerical results show that this method is more effective than the norm method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Bertsekas DP (1995) Nonlinear programming. Athena Scientific, Belmont

    MATH  Google Scholar 

  2. Bing Y, Lin G (1991) An efficient implementation of Merrill’s method for sparse or partially separable systems of nonlinear equations. SIAM J Optim 2: 206–221

    Article  Google Scholar 

  3. Conn AR, Gould NIM, Toint PL (2000) Trust region method. Society for Industrial and Applied Mathematics, Philadelphia

    Book  Google Scholar 

  4. Dennis JE, Moré JJ (1974) A characterization of superlinear convergence and its application to quasi-Newton methods. Math Comput 28: 549–560

    Article  MATH  Google Scholar 

  5. Dolan ED, Moré JJ (2002) Benchmarking optimization software with performance profiles. Math Program 91: 201–213

    Article  MathSciNet  MATH  Google Scholar 

  6. Fan JY (2003) A modified Levenberg-Marquardt algorithm for singular system of nonlinear equations. J Comput Math 21: 625–636

    MathSciNet  MATH  Google Scholar 

  7. Gomez-Ruggiero M, Martinez JM, Moretti A (1992) Comparing algorithms for solving sparse nonlinear systems of equations. SIAM J Sci Stat Comput 23: 459–483

    Google Scholar 

  8. Griewank A (1986) The ‘global’ convergence of Broyden-like methods with a suitable line search. J Aust Math Soc Ser B 28: 75–92

    Article  MathSciNet  MATH  Google Scholar 

  9. Levenberg K (1944) A method for the solution of certain nonlinear problem in least squares. Q Appl Math 2: 164–166

    MathSciNet  MATH  Google Scholar 

  10. Li D, Fukushima M (1999) A global and superlinear convergent Gauss-Newton-based BFGS method for symmetric nonlinear equations. SIAM J Numer Anal 37: 152–172

    Article  MathSciNet  MATH  Google Scholar 

  11. Li D, Qi L, Zhou S (2002) Descent directions of quasi-Newton methods for symmetric nonlinear equations. SIAM J Numer Anal 40(5): 1763–1774

    Article  MathSciNet  MATH  Google Scholar 

  12. Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear inequalities. SIAM J Appl Math 11: 431–441

    Article  MathSciNet  MATH  Google Scholar 

  13. Moré JJ (1983) Recent development in algorithm and software for trust region methods. In: Bachem A, Grotschel M, Kortz B (eds) Mathematical programming: the state of the art. Spinger, Berlin, pp 258–285

    Google Scholar 

  14. Moré JJ, Garbow BS, Hillström KE (1981) Testing unconstrained optimization software. ACM Trans Math Softw 7: 17–41

    Article  MATH  Google Scholar 

  15. Nocedal J, Wright SJ (1999) Numerical optimization. Spinger, New York

    Book  MATH  Google Scholar 

  16. Ortega JM, Rheinboldt WC (1970) Iterative solution of nonlinear equations in several variables. Academic Press, New York

    MATH  Google Scholar 

  17. Raydan M (1997) The Barzilai and Borwein gradient method for the large scale unconstrained minimization problem. SIAM J Optim 7: 26–33

    Article  MathSciNet  MATH  Google Scholar 

  18. Wang YJ, Xiu NH (2004) Theory and algoithm for nonlinear programming. Shanxi Science and Technology Press, Xian

    Google Scholar 

  19. Wei Z, Yuan G, Lian Z (2004) An approximate Gauss-Newton-based BFGS method for solving symmetric nonlinear equations. Guangxi Sci 11(2): 91–99

    Google Scholar 

  20. Yamashita N, Fukushima M (2001) On the rate of convergence of the Levenberg-Marquardt Method. Computing 15: 239–249

    MathSciNet  Google Scholar 

  21. Yuan Y (1998) Trust region algorithm for nonlinear equations. Information 1: 7–21

    MathSciNet  MATH  Google Scholar 

  22. Yuan G, Chen C, Wei Z (2010) A nonmonotone adaptive trust-region algorithm for symmetric nonlinear equations. Nat Sci 2(4): 373–378

    Google Scholar 

  23. Yuan G, Li X (2004) An approximate Gauss-Newton-based BFGS method with descent directions for solving symmetric nonlinear equations. OR Trans 8: 10–26

    Google Scholar 

  24. Yuan G, Li X (2010) A rank-one fitting method for solving symmetric nonlinear equations. J Appl Funct Anal 5: 389–407

    MathSciNet  MATH  Google Scholar 

  25. Yuan G, Lu X (2008) A new backtracking inexact BFGS method for symmetric nonlinear equations. Comput Math Appl 55: 116–129

    Article  MathSciNet  MATH  Google Scholar 

  26. Yuan G, Lu X, Wei Z (2009) BFGS trust-region method for symmetric nonlinear equations. J Comput Appl Math 230(1): 44–58

    Article  MathSciNet  MATH  Google Scholar 

  27. Yuan G, Meng S, Wei Z (2009) A trust-region-based BFGS method with line search technique for symmetric nonlinear equations. Adv Oper Res 2009: 1–20

    Article  Google Scholar 

  28. Yuan G, Wang Z, Wei Z (2009) A rank-one fitting method with descent direction for solving symmetric nonlinear equations. Int J Commun Netw Syst Sci 6: 555–561

    Google Scholar 

  29. Yuan G, Wei Z, Lu X (2009) A nonmonotone trust region method for solving symmetric nonlinear equations. Chin Q J Math 24(4): 574–584

    MathSciNet  MATH  Google Scholar 

  30. Yuan G, Sun W (1997) Optimization theory and methods. Science Press, Beijing

    Google Scholar 

  31. Zhang J, Wang Y (2003) A new trust region method for nonlinear equations. Math Methods Oper Res 58: 283–298

    Article  MathSciNet  MATH  Google Scholar 

  32. Zhu D (2005) Nonmonotone backtracking inexact quasi-Newton algorithms for solving smooth nonlinear equations. Appl Math Comput 161: 875–895

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gonglin Yuan.

Additional information

This work is supported by China NSF grands 10761001, the Scientific Research Foundation of Guangxi University (Grant No. X081082), and Guangxi SF grands 0991028.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yuan, G., Wei, Z. & Lu, X. A BFGS trust-region method for nonlinear equations. Computing 92, 317–333 (2011). https://doi.org/10.1007/s00607-011-0146-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-011-0146-z

Keywords

Mathematics Subject Classification (2000)

Navigation