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Global Convergence Property of Scaled Two-Step BFGS Method

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Abstract

This paper is aimed to extend the scheme of self scaling, appropriate for the quasi-Newton methods, to the two-step quasi-Newton methods. The scaling scheme has been performed during the main approach of updating the current Hessian approximation and prior to the computation of the next quasi-Newton direction whenever necessary. Global convergence property of the new method is explored on uniformly convex functions with the standard Wolfe line search. Preliminary numerical testing has been performed showing that this technique improves the performance of the two-step method substantially.

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References

  1. Al-Baali, M.: Extra updates for the BFGS method. Optim. Method. Softw. 13, 159–179 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  2. Al-Baali, M.: Global and superlinear convergence of a restricted class of self-scaling methods with inexact line search, for convex functions. Comput. Optim. Appl. 9, 191–203 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  3. Al-Baali, M.: New initial Hessian approximations for the limited memory BFGS method for large scale optimization. J. Fac. Sci., UAE University 1(4), 167–175 (1995)

  4. Al-Baali, M.: Variational quasi-Newton methods for unconstrained optimization. J. Optim. Theory Appl. 7(l), 127–143 (1993)

  5. Al-Baali, M., Conforti, D., Musmanno, R.: Computational experiments with scaled initial Hessian approximation for the Broyden family methods. Optim. A. Oper. Res. 48, 375–389 (2000)

    MathSciNet  MATH  Google Scholar 

  6. Al-Baali, M., Grandinetti, L.: Improved damped quasi-Newton methods for unconstrained optimization. In: Fourth Asian conference on nonlinear analysis and optimization, (NAO-Asia), Taipei, Taiwan, Aug 5–9 (2014)

  7. Al-Baali, M., Khalfan, H.F.: A combined class of self-scaling and modified quasi-Newton methods. Comput. Optim. Appl. 52(2), 393–408 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  8. Andrei, N.: An unconstrained optimization test functions collection. Adv. Model. Optim. 10, 147–161 (2008)

    MathSciNet  MATH  Google Scholar 

  9. Byrd, R.H., Liu, D.C., Nocedal, J.: On the behaviour of Broyden’s class of quasi-Newton methods. SIAM J. Optim. 2, 533–557 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  10. Byrd, R.H., Nocedal, J., Yuan, Y.: Global convergence of a class of quasi- Newton methods on convex problems. SIAM J. Numer. Anal. 24, 1171–1190 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  11. Dennis, J.E., Wolkowicz, H.: Sizing and least change secant methods. Technical Report, Department of Mathematical Sciences, Rice University (Houston,TX) (1991)

  12. Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–203 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  13. Fletcher, R.: Practical Methods of Optimization, 2nd edn. Wiley, NewYork (1987)

    MATH  Google Scholar 

  14. Ford, J.A.: Implicit updates in multistep quasi-Newton methods. Comput. Math. Appl. 42, 1083–1091 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  15. Ford, J.A., Moghrabi, I.A.: Alternating multi-step quasi-Newton methods for unconstrained optimization. J. Compt. Appl. Math. 82, 105–116 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  16. Ford, J.A., Moghrabi, I.A.: Multi-step quasi-Newton methods for optimization. J. Comput. Appl. Math. 50, 305–323 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  17. Ford, J.A., Moghrabi, I.A.: Alternative parameter choices for multi-step quasi-Newton methods. Optim. Methods Softw. 2, 357–370 (1993)

    Article  Google Scholar 

  18. Ford, J.A., Moghrabi, I.A.: Further investigation of multi-step quasi-Newton methods. Sci. Iran. 1, 327–334 (1995)

    MathSciNet  MATH  Google Scholar 

  19. Foroutan, M., Ebadian, A., Biglari, F.: Existence and approximate solutions of nonlinear boundary value problems on the half line. Int. J. Numer. Meth. Heat Fluid Flow (2017) (submitted)

  20. Nocedal, J., Wright, S.J.: Springer Series in Operations Research. Numerical Optimization, 2nd edn. Springer, New York (2006)

  21. Oren, S.S.: On the selection of parameters in self-scaling variable metric algorithms. Math. Program. 3, 351–367 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  22. Oren, S.S., Luenberger, D.G.: Self-scaling variable metric algorithm. Part I. Manag. Sci. 20, 845–862 (1974)

    Article  MATH  Google Scholar 

  23. Oren, S.S., Spedicato, E.: Optimal conditioning of self-scaling variable metric algorithms. Math. Program. 10, 70–90 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  24. Pearson, J.D.: Variable metric methods of minimization. Comput. J. 12, 171–178 (1969)

    Article  MathSciNet  MATH  Google Scholar 

  25. Powell, M.J.D.: How bad are the BFGS and DFP methods when the objective function is quadratic? Math. Program. 34, 34–47 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  26. Shanno, D.F.: Conditioning of quasi-Newton methods for function minimization. Math. Comput. 24, 647–656 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  27. Shanno, D.F.: Conditioning of quasi-Newton methods for function minimization. Math. Comput. 24, 647–656 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  28. Shanno, D.F., Phua, K.H.: Matrix conditioning and nonlinear optimization. Math. Program. 14, 149–160 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  29. Spedicato, E.: Computational experience with quasi-Newton algorithms for minimization problems of moderately large size. Report CISE-N-175, CISE Documentation Service, Segrate (Milano) (1975)

  30. Spedicato, E.: Stability of Huang’s update for the conjugate gradient method. J. Optim. Theory Appl. 11, 469–479 (1973)

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Fahimeh Biglari.

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Biglari, F., Ebadian, A. & Foroutan, M. Global Convergence Property of Scaled Two-Step BFGS Method. Mediterr. J. Math. 15, 11 (2018). https://doi.org/10.1007/s00009-017-1060-1

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  • DOI: https://doi.org/10.1007/s00009-017-1060-1

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