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Cited by 364

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

Dud, A Derivative-Free Algorithm for Nonlinear Least Squares

Ralston, Mary L., Jennrich, Robert I.·1978·Technometrics
DOI 10.1080/00401706.1978.10489610
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Abstract

Derivative-free nonlinear least squares algorithms which make efficient use of function evaluations are important for fitting models defined by systems of nonlinear differential equations. A new Gauss-Newton-like algorithm with these properties is developed. The performance of the new algorithm (called Dud for “doesn't use derivatives”) is evaluated on a number of standard test problems from the literature. On these problems Dud competes favorably with even the best derivative-based algorithms.

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Metadata

Authors
Ralston, M. L., & Jennrich, R. I.
Venue
Technometrics
Publisher
Informa UK Limited
Published
1978-02
Volume
20
Issue
1
Pages
7-14
ISSN
  • 0040-1706 (ISSN-L)
  • 1537-2723
DOI
10.1080/00401706.1978.10489610
OpenAlex
W1987564911
Cited by
364
References
26
FWCI
6.21
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Dud, A Derivative-Free Algorithm for Nonlinear Least Squares. https://app.cochat.ai/papers/10.1080/00401706.1978.10489610
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Ralston, M. L., & Jennrich, R. I. (1978). Dud, A Derivative-Free Algorithm for Nonlinear Least Squares. Technometrics. https://doi.org/10.1080/00401706.1978.10489610

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.