The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A trust region method based on interior point techniques for nonlinear programming
Open accessAbstract
Abstract.An algorithm for minimizing a nonlinear function subject to nonlinear inequality constraints is described. It applies sequential quadratic programming techniques to a sequence of barrier problems, and uses trust regions to ensure the robustness of the iteration and to allow the direct use of second order derivatives. This framework permits primal and primal-dual steps, but the paper focuses on the primal version of the new algorithm. An analysis of the convergence properties of this method is presented.
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Citations
Cites
An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds
Coleman, T. F., & Li, Y. (1996) · SIAM Journal on Optimization · doi:10.1137/0806023 · Cited by 3,320
An Interior Point Algorithm for Large-Scale Nonlinear Programming
Byrd, R. H., Hribar, M. E., & Nocedal, J. (1999) · SIAM Journal on Optimization · doi:10.1137/s1052623497325107 · Cited by 1,739
Self-citationOptimal reactive dispatch through interior point methods
Granville, S. (1994) · IEEE Transactions on Power Systems · doi:10.1109/59.317548 · Cited by 876
The Conjugate Gradient Method and Trust Regions in Large Scale Optimization
Steihaug, T. (1983) · SIAM Journal on Numerical Analysis · doi:10.1137/0720042 · Cited by 824
On the formulation and theory of the Newton interior-point method for nonlinear programming
El-Bakry, A. S., Tapia, R. A., Tsuchiya, T., & Zhang, Y. (1996) · Journal of Optimization Theory and Applications · doi:10.1007/bf02275347 · Cited by 363
A Trust Region Algorithm for Nonlinearly Constrained Optimization
Byrd, R. H., Schnabel, R. B., & Shultz, G. A. (1987) · SIAM Journal on Numerical Analysis · doi:10.1137/0724076 · Cited by 353
Self-citationPath-Following Methods for Linear Programming
Gonzaga, C. C. (1992) · SIAM Review · doi:10.1137/1034048 · Cited by 341
Feature Article—Interior Point Methods for Linear Programming: Computational State of the Art
Lustig, I. J., Marsten, R. E., & Shanno, D. F. (1994) · INFORMS journal on computing · doi:10.1287/ijoc.6.1.1 · Cited by 315
Vardi, A. (1985) · SIAM Journal on Numerical Analysis · doi:10.1137/0722035 · Cited by 139
Trust region algorithms for optimization with nonlinear equality and inequality constraints
Omojokun, E. O. (1990) · Cited by 133
Cited by
Wächter, A., & Biegler, L. T. (2006) · Mathematical Programming · doi:10.1007/s10107-004-0559-y · Cited by 9,483
Variations and extension of the convex–concave procedure
Lipp, T., & Boyd, S. (2016) · Optimization and Engineering · doi:10.1007/s11081-015-9294-x · Cited by 744
Ten simple rules for the computational modeling of behavioral data
Wilson, R. C., & Collins, A. G. (2019) · eLife · doi:10.7554/elife.49547 · Cited by 710
Interior Methods for Nonlinear Optimization
Forsgren, A., Gill, P. E., & Wright, M. H. (2002) · SIAM Review · doi:10.1137/s0036144502414942 · Cited by 699
Model Predictive Control of Swarms of Spacecraft Using Sequential Convex Programming
Morgan, D., Chung, S.-J., & Hadaegh, F. Y. (2014) · Journal of Guidance, Control, and Dynamics · doi:10.2514/1.g000218 · Cited by 384
Metadata
- Authors
- Byrd, R. H., Gilbert, J. C., & Nocedal, J.
- Venue
- Mathematical Programming
- Published
- 2000-11
- Volume
- 89
- Issue
- 1
- Pages
- 149-185
- ISSN
- 0025-5610 (ISSN-L)
- OpenAlex
- W2041684917
- Cited by
- 1,638
- References
- 14
- FWCI
- 24.33
- Type
- journal article
- Language
- en
- Open access
- green
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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.
