The DOI matches its Crossref record.
Introduction to Numerical Continuation Methods
Abstract
From the Publisher: Introduction to Numerical Continuation Methods continues to be useful for researchers and graduate students in mathematics, sciences, engineering, economics, and business looking for an introduction to computational methods for solving a large variety of nonlinear systems of equations. A background in elementary analysis and linear algebra is adequate preparation for reading this book; some knowledge from a first course in numerical analysis may also be helpful.
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Cited by
Score-Based Generative Modeling through Stochastic Differential Equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2020) · arXiv · doi:10.48550/arxiv.2011.13456 · Cited by 1,953
Peeters, M., Viguié, R., Sérandour, G., Kerschen, G., & Golinval, J.-C. (2009) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2008.04.003 · Cited by 543
Saa, P. A., & Nielsen, L. K. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.09.005 · Cited by 166
Metadata
- Authors
- Allgower, E. L., & Georg, K.
- Venue
- Society for Industrial and Applied Mathematics eBooks
- Published
- 2003-01
- OpenAlex
- W1607862333
- Cited by
- 876
- References
- 0
- FWCI
- 2.38
- Type
- book
- Language
- en
- Open access
- closed
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- Allgower, E. L., & Georg, K. (2003). Introduction to Numerical Continuation Methods. Society for Industrial and Applied Mathematics eBooks. https://doi.org/10.1137/1.9780898719154
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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.
