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Numerical experience with a class of algorithms for nonlinear optimization using inexact function and gradient informationFor optimization problems associated with engineering design, parameter estimation, image reconstruction, and other optimization/simulation applications, low accuracy function and gradient values are frequently much less expensive to obtain than high accuracy values. Here, researchers investigate the computational performance of trust region methods for nonlinear optimization when high accuracy evaluations are unavailable or prohibitively expensive, and confirm earlier theoretical predictions when the algorithm is convergent even with relative gradient errors of 0.5 or more. The proper choice of the amount of accuracy to use in function and gradient evaluations can result in orders-of-magnitude savings in computational cost.
Document ID
19900002916
Acquisition Source
Legacy CDMS
Document Type
Preprint (Draft being sent to journal)
Authors
Carter, Richard G. (Institute for Computer Applications in Science and Engineering Hampton, VA, United States)
Date Acquired
September 6, 2013
Publication Date
June 1, 1989
Subject Category
Numerical Analysis
Report/Patent Number
NAS 1.26:181927
ICASE-89-46
NASA-CR-181927
Accession Number
90N12232
Funding Number(s)
CONTRACT_GRANT: NAS1-18605
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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