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Convergence Rate of The Trust Region Method for Nonlinear Equations Under Local Error Bound Condition

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An Erratum to this article was published on 14 August 2006

Abstract

In this paper, we present the new trust region method for nonlinear equations with the trust region converging to zero. The new method preserves the global convergence of the traditional trust region methods in which the trust region radius will be larger than a positive constant. We study the convergence rate of the new method under the local error bound condition which is weaker than the nonsingularity. An example given by Y.X. Yuan shows that the convergence rate can not be quadratic. Finally, some numerical results are given.

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References

  1. Y.H. Dai and D. Xu, “A new family of trust region algorithms for unconstrained optimization,” Journal of Computational Mathematics, vol. 21, no. 2, pp. 221–228, 2003.

    MATH  MathSciNet  Google Scholar 

  2. J.Y. Fan, “A modified Levenberg-Marquardt algorithm for singular system of nonlinear equations,” Journal of Computational Mathematics, vol. 21, no. 5, pp. 625–636, 2003.

    MATH  MathSciNet  Google Scholar 

  3. J.Y. Fan and Y.X. Yuan, “On the quadratic convergence of the Levenberg-Marquardt method without nonsingularity assumption,” to appear in Computing.

  4. J.Y. Fan and Y.X. Yuan, “A new trust region algorithm with trust region radius converging to zero,” in: D. Li di (Ed.), Proceedings of the 5th International Conference on Optimization: Techniques and Applications, Hongkong, Dec. 2001, pp. 786–794.

  5. K. Levenberg, “A method for the solution of certain nonlinear problems in least squares,” Quart. Appl. Math., vol. 2, pp. 164–166, 1944.

    MATH  MathSciNet  Google Scholar 

  6. D.W. Marquardt, “An algorithm for least-squares estimation of nonlinear inequalities,” SIAM J. Appl. Math., vol. 11, pp. 431–441, 1963.

    Article  MATH  MathSciNet  Google Scholar 

  7. J.J. Moré, “The Levenberg-Marquardt algorithm: Implementation and theory,” in: G.A. Watson (Ed.), Lecture Notes in Mathematics 630: Numerical Analysis, Springer-Verlag, Berlin, 1978, pp. 105–116.

  8. J.J. Moré, B.S. Garbow, and K.H. Hillstrom, “Testing unconstrained optimization software,” ACM Trans. Math. Software, vol. 7, pp. 17–41, 1981.

    Article  MATH  MathSciNet  Google Scholar 

  9. J.J. Moré, “Recent developments in algorithms and software for trust region methods,” in: A. Bachem, M. Gr⊙tschel, and B. Korte (Eds.), Mathematical Programming: The State of Art, Springer, Berlin, 1983, pp. 258–287, 1883a.

  10. J.J. Moré and D.C. Sorensen, “Computing a trust region step,” SIAM J. Sci. Stat. Comp., vol. 4, pp. 553–572, 1983,1983b.

    Article  Google Scholar 

  11. J. Nocedal and Y.X. Yuan, “Combining trust region and line search techniques,” in: Y. Yuan (Ed.), Advances in Nonlinear Programming, Kluwer, 1998, pp. 153–175.

  12. M.J.D. Powell, “Convergence properties of a class of minimization algorithms,” in: O.L. Mangasarian, R.R. Meyer, and S. M. Robinson (Eds.), Nonlinear Programming, Academic Press, New York, 1975, pp. 1–27.

  13. R.B. Schnabel and P.D. Frank, “Tensor methods for nonlinear equations,” SIAM J. Numer, Anal., vol. 21, pp. 815–843, 1984.

    Article  MATH  MathSciNet  Google Scholar 

  14. D. Xu, J. Han, and Z. Chen, “Nonmonotone trust-region method for nonlinear programming with general constraints and simple bounds,” Journal of Optimization Theory and Applications, vol. 122, no. 1, pp. 185–206, 2004.

    Article  MathSciNet  Google Scholar 

  15. N. Yamashita and M. Fukushima, “On the rate of convergence of the Levenberg-Marquardt method,” Computing (Supplement 15), pp. 237–249, 2001.

  16. Y.X. Yuan, “Trust region algorithms for nonlinear programming,” in: Z.C. SHi (Ed.), Contemporary Mathematics vol. 163, American Mathematics Society, 1994, pp. 205–225.

  17. Y.X. Yuan, “Trust region algorithms for nonlinear equations,” Information, vol. 1, pp. 7–20, 1998.

    MATH  MathSciNet  Google Scholar 

  18. Y.X. Yuan, “A review of trust region algorithms for optimization,” in: J.M. Ball and J.C.R. Hunt (Eds.), ICM99: Proceedings of the Fourth International Congress on Industrial and Applied Mathematics, Oxford University Press, 2000, pp. 271–282, 2000a.

  19. Y.X. Yuan, Private communication, 2004.

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This work is supported by Chinese NSFC grants 10401023 and 10371076, Research Grants for Young Teachers of Shanghai Jiao Tong University, and E-Institute of Computational Sciences of Shanghai Universities.

An erratum to this article is available at http://dx.doi.org/10.1007/s10589-006-9594-3.

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Fan, J. Convergence Rate of The Trust Region Method for Nonlinear Equations Under Local Error Bound Condition. Comput Optim Applic 34, 215–227 (2006). https://doi.org/10.1007/s10589-005-3078-8

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