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Conditioning of quasi-Newton methods for function minimization
Open accessAbstract
Quasi-Newton methods accelerate the steepest-descent technique for function minimization by using computational history to generate a sequence of approximations to the inverse of the Hessian matrix. This paper presents a class of approximating matrices as a function of a scalar parameter. The problem of optimal conditioning of these matrices under an appropriate norm as a function of the scalar parameter is investigated. A set of computational results verifies the superiority of the new methods arising from conditioning considerations to known methods.
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Metadata
- Authors
- Shanno, D. F.
- Venue
- Mathematics of Computation
- Publisher
- American Mathematical Society (AMS)
- Published
- 1970
- Volume
- 24
- Issue
- 111
- Pages
- 647-656
- ISSN
- 0025-5718 (ISSN-L)
- 1088-6842
- OpenAlex
- W2005136695
- Cited by
- 3,872
- References
- 0
- FWCI
- 6.49
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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