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Design and Testing of a Generalized Reduced Gradient Code for Nonlinear Programming
Abstract
The purpose of this paper is to describe a Generalized Reduced Gradient (GRG) algorithm for nonlinear programming, its implementation as a FORTRAN program for solving small to medium size problems, and some computational results. Our focus is more on the software implementation of the algorithm than on its mathematical properties. This is in line with the premise that robust, efficient, easy to use NLP software must be written and made accessible if nonlinear programming is to progress, both in theory and in practice.
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Cited by
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Sharpe, K. M., & Staelin, R. (2010) · Journal of Public Policy & Marketing · doi:10.1509/jppm.29.2.170 · Cited by 72
A study of frost build-up on hydrophilic and hydrophobic surfaces under forced convection conditions
Hermes, C. J. L., Nascimento, V. S., Loyola, F. R., Cardoso, R. P., & Sommers, A. D. (2019) · Experimental Thermal and Fluid Science · doi:10.1016/j.expthermflusci.2018.08.009 · Cited by 44
Metadata
- Authors
- Lasdon, L. S., Waren, A. D., Jain, A., & Ratner, M.
- Venue
- ACM Transactions on Mathematical Software
- Published
- 1978-03
- Volume
- 4
- Issue
- 1
- Pages
- 34-50
- ISSN
- 0098-3500 (ISSN-L)
- 1557-7295
- OpenAlex
- W2074145107
- Cited by
- 1,282
- References
- 0
- FWCI
- 9.32
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Design and Testing of a Generalized Reduced Gradient Code for Nonlinear Programming. https://app.cochat.ai/papers/10.1145/355769.355773
- The paper
- Lasdon, L. S., Waren, A. D., Jain, A., & Ratner, M. (1978). Design and Testing of a Generalized Reduced Gradient Code for Nonlinear Programming. ACM Transactions on Mathematical Software. https://doi.org/10.1145/355769.355773
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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.
