Skip to main content

An Example of Non-Convergence of Trust Region Algorithms

  • Chapter
Advances in Nonlinear Programming

Part of the book series: Applied Optimization ((APOP,volume 14))

Abstract

It is well known that trust region algorithms have very nice convergence properties. Descent trust region algorithms can be classified into two groups. The first can be called “sufficient reduction methods” where the condition for accepting a new point is a sufficient reduction in the merit function. The other can be called “simple reduction” methods where they accept a new point as long as it reduces the merit function. In general, it can be shown that the algorithms that require sufficient reductions have strong convergence result, namely all accumulation points are stationary points. Though “simple reduction” methods have the nice property of accepting any better iterates, convegence results for these algorithms are weaker than thoes for “sufficient reduction” methods, as we are only able to show that at least one accumulation point is a stationary point.

In this paper, we construct an example to show that “simple reduction” algorithms may generate a sequence that does not converge. Instead, the sequence cycles nearly three points where only one of them is a stationary point.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. N.Y. Deng, Y. Xiao and F.J. Zhou, “Nonmonotonic trust region algorithms”,J. Opt Theory and Appl. 76 (1993) 259–285.

    Article  MathSciNet  MATH  Google Scholar 

  2. I.S. Duff, J. Nocedal, and J.K. Reid, “The use of linear programming for the solution of sparse sets of nonlinear equations”,SIAM J.Sci. Stat. Comput. 8 (1987) 99–108.

    Article  MathSciNet  MATH  Google Scholar 

  3. R. Fletcher, “A model algorithm for composite NDO problem”,Math. Prog. Study 17 (1982) 67–76. (1982a)

    MathSciNet  MATH  Google Scholar 

  4. R. Fletcher,Practical Methods of Optimization (second edition) ( John Wiley and Sons, Chichester, 1987 )

    MATH  Google Scholar 

  5. J.J. Moré, “Recent developments in algorithms and software for trust region methods”, in: A. Bachem, M. Grotschel and B. Korte, eds.,Mathematical Programming: The State of the Art ( Springer–Verlag, Berlin, 1983 ) pp. 258–287.

    Google Scholar 

  6. 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 2 ( Acadmic Press, New York, 1975 ) pp. 1–27.

    Google Scholar 

  7. M.J.D. Powell, “Nonconvex minimization calculations and the conjugate gradient method”, in: D.F. Griffiths, ed.,Numerical Analysis Lecture Notes in Mathematics 1066 (Springer–Verlag, Berlin, 1984 ) pp. 122–141.

    Chapter  Google Scholar 

  8. D.C. Sorensen, “Newton’s method with a model trust region modification”,SI AM J. Numer. Anal. 20 (1982) 409–426.

    Article  MathSciNet  Google Scholar 

  9. Y. Yuan,Trust Region Algorithms, (unpublished manuscript, 1993 ).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Kluwer Academic Publishers

About this chapter

Cite this chapter

Yuan, Yx. (1998). An Example of Non-Convergence of Trust Region Algorithms. In: Yuan, Yx. (eds) Advances in Nonlinear Programming. Applied Optimization, vol 14. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-3335-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4613-3335-7_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-3337-1

  • Online ISBN: 978-1-4613-3335-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics