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Combining Trust Region and Line Search Techniques

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Advances in Nonlinear Programming

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

Abstract

We propose an algorithm for nonlinear optimization that employs both trust region techniques and line searches. Unlike traditional trust region methods, our algorithm does not resolve the subproblem if the trial step results in an increase in the objective function, but instead performs a backtracking line search from the failed point. Backtracking can be done along a straight line or along a curved path. We show that the new algorithm preserves the strong convergence properties of trust region methods. Numerical results are also presented.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208-3118, USA

    Jorge Nocedal

  2. State Key Laboratory of Scientific and Engineering Computing, Institute of Computational Mathematics and Scientific/Engineering Computing, Chinese Academy of Sciences, Beijing, 100080, China

    Ya-xiang Yuan

Authors
  1. Jorge Nocedal
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  2. Ya-xiang Yuan
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Editor information

Editors and Affiliations

  1. Institute of Computational Mathematics, and Scientific/Engineering Computing, Chinese Academy of Sciences, Beijing, China

    Ya-xiang Yuan

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© 1998 Kluwer Academic Publishers

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Nocedal, J., Yuan, Yx. (1998). Combining Trust Region and Line Search Techniques. 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_7

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  • DOI: https://doi.org/10.1007/978-1-4613-3335-7_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-3337-1

  • Online ISBN: 978-1-4613-3335-7

  • eBook Packages: Springer Book Archive

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