Automatic determination of an initial trust region in nonlinear programming

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Abstract

This paper presents a simple but efficient way to find a good initial trust region radius in trust region methods for nonlinear optimization. The method consists of monitoring the agreement between the model and the objective function along the steepest descent direction, computed at the starting point. Further improvements for the starting point are also derived from the information gleaned during the initializing phase. Numerical results on a large set of problems show the impact the initial trust region radius may have on trust region methods behaviour and the usefulness of the proposed strategy.
Original languageEnglish
Pages (from-to)1788-1803
Number of pages16
JournalSIAM Journal on Scientific Computing
Volume18
Issue number6
Publication statusPublished - 1997

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Trust Region
Nonlinear programming
Nonlinear Programming
Radius
Trust Region Method
Monitoring
Large Set
Objective function
Numerical Results
Model

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title = "Automatic determination of an initial trust region in nonlinear programming",
abstract = "This paper presents a simple but efficient way to find a good initial trust region radius in trust region methods for nonlinear optimization. The method consists of monitoring the agreement between the model and the objective function along the steepest descent direction, computed at the starting point. Further improvements for the starting point are also derived from the information gleaned during the initializing phase. Numerical results on a large set of problems show the impact the initial trust region radius may have on trust region methods behaviour and the usefulness of the proposed strategy.",
author = "Annick Sartenaer",
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journal = "SIAM Journal on Scientific Computing",
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}

Automatic determination of an initial trust region in nonlinear programming. / Sartenaer, Annick.

In: SIAM Journal on Scientific Computing, Vol. 18, No. 6, 1997, p. 1788-1803.

Research output: Contribution to journalArticle

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AB - This paper presents a simple but efficient way to find a good initial trust region radius in trust region methods for nonlinear optimization. The method consists of monitoring the agreement between the model and the objective function along the steepest descent direction, computed at the starting point. Further improvements for the starting point are also derived from the information gleaned during the initializing phase. Numerical results on a large set of problems show the impact the initial trust region radius may have on trust region methods behaviour and the usefulness of the proposed strategy.

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