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A primal truncated newton algorithm with application to large-scale nonlinear network optimization

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Computation Mathematical Programming

Part of the book series: Mathematical Programming Studies ((MATHPROGRAMM,volume 31))

Abstract

We describe a new, convergent, primal-feasible algorithm for linearly constrained optimization. It is capable of rapid asymptotic behavior and has relatively low storage requirements. Its application to large-scale nonlinear network optimization is discussed and computational results on problems of over 2000 variables and 1000 constraints are presented. Indications are that it could prove to be significantly better than known methods for this class of problems.

This was research supported in part by DOT Grant CT-06-0011 and by NSF Grant ECS-8119513.

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Authors and Affiliations

  1. Yale University, 06520, New Haven, CT, USA

    Ron. S. Dembo

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  1. Ron. S. Dembo
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Editor information

K. L. Hoffman R. H. F. Jackson J. Telgen

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© 1987 The Mathematical Programming Society, Inc.

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Dembo, R.S. (1987). A primal truncated newton algorithm with application to large-scale nonlinear network optimization. In: Hoffman, K.L., Jackson, R.H.F., Telgen, J. (eds) Computation Mathematical Programming. Mathematical Programming Studies, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0121178

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  • DOI: https://doi.org/10.1007/BFb0121178

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00932-7

  • Online ISBN: 978-3-642-00933-4

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