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System Modelling and Optimization pp 187–196Cite as

Newton-type algorithms with nonmonotone line search for large-scale unconstrained optimization

  • Continuous Estimation
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Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 113))

Abstract

In this paper we define globally convergent algorithms for the solution of large dimensional unconstrained minimization problems. The algorithms proposed employ a nonmonotone steplength selection rule along the search direction which is determined by means of a Truncated-Newton algorithm. Numerical results obtained for a set of test problems are reported.

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References

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Author information

Authors and Affiliations

  1. Istituto di Analisi dei Sistemi ed Informatica del CNR, Viale Manzoni 30, 00185, Roma, Italy

    L. Grippo, F. Lampariello & S. Lucidi

Authors
  1. L. Grippo
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  2. F. Lampariello
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  3. S. Lucidi
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Editor information

Masao Iri Keiji Yajima

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© 1988 International Federation for Information Processing

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Grippo, L., Lampariello, F., Lucidi, S. (1988). Newton-type algorithms with nonmonotone line search for large-scale unconstrained optimization. In: Iri, M., Yajima, K. (eds) System Modelling and Optimization. Lecture Notes in Control and Information Sciences, vol 113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0042786

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  • DOI: https://doi.org/10.1007/BFb0042786

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-19238-1

  • Online ISBN: 978-3-540-39164-7

  • eBook Packages: Springer Book Archive

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