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Updating quasi-Newton matrices with limited storage


Author: Jorge Nocedal
Journal: Math. Comp. 35 (1980), 773-782
MSC: Primary 65K05; Secondary 90C30
MathSciNet review: 572855
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Abstract: We study how to use the BFGS quasi-Newton matrices to precondition minimization methods for problems where the storage is critical. We give an update formula which generates matrices using information from the last m iterations, where m is any number supplied by the user. The quasi-Newton matrix is updated at every iteration by dropping the oldest information and replacing it by the newest information. It is shown that the matrices generated have some desirable properties.

The resulting algorithms are tested numerically and compared with several well-known methods.


References [Enhancements On Off] (What's this?)

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Additional Information

DOI: https://doi.org/10.1090/S0025-5718-1980-0572855-7
Article copyright: © Copyright 1980 American Mathematical Society