The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Practical Bilevel Optimization
Abstract
The focus of this book is on bilevel programming which combines elements of hierarchical optimization and game theory. The basic model addresses the problem where two decision-makers, each with their individual objectives, act and react in a noncooperative manner. The actions of one affect the choices and payoffs available to the other but neither player can completely dominate the other in the traditional sense. Over the last 20 years there has been a steady growth in research related to theory and solution methodologies for bilevel programming. This interest stems from the inherent complexity and consequent challenge of the underlying mathematics, as well as the applicability of the bilevel model to many real-world situations. The primary aim of this book is to provide a historical perspective on algorithmic development and to highlight those implementations that have proved to be the most efficient in their class. A corollary aim is to provide a sampling of applications in order to demonstrate the versatility of the basic model and the limitations of current technology. What is unique about this book is its comprehensive and integrated treatment of theory, algorithms and implementation issues. It is the first text that offers researchers and practitioners an elementary understanding of how to solve bilevel programs and a perspective on what success has been achieved in the field. Audience: Includes management scientists, operations researchers, industrial engineers, mathematicians and economists.
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Citations
Cites
A new polynomial-time algorithm for linear programming
Karmarkar, N. (1984) · Combinatorica · doi:10.1007/bf02579150 · Cited by 4,794
Linear Programming and Network Flows
Sergeant, C. R. (1978) · Journal of the Operational Research Society · doi:10.1057/jors.1978.105 · Cited by 1,973
Linear Programming: Foundations and Extensions
Vanderbei, R. J. (1998) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600987 · Cited by 921
Cited by
Burgard, A. P., Pharkya, P., & Maranas, C. D. (2003) · Biotechnology and Bioengineering · doi:10.1002/bit.10803 · Cited by 1,256
Certified Defenses for Data Poisoning Attacks
Steinhardt, J., Koh, P. W., & Liang, P. (2017) · arXiv · doi:10.48550/arxiv.1706.03691 · Cited by 479
A review of recent advances in global optimization
Floudas, C. A., & Gounaris, C. E. (2009) · Journal of Global Optimization · doi:10.1007/s10898-008-9332-8 · Cited by 455
Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners
Mei, S., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9569 · Cited by 346
Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications
Li, X., Epitropakis, M. G., Deb, K., & Engelbrecht, A. (2017) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2638437 · Cited by 339
Metadata
- Authors
- Bard, J. F.
- Venue
- Nonconvex Optimization and Its Applications
- Publisher
- Springer US
- Published
- 1998
- ISSN
- 1571-568X
- OpenAlex
- W1676470423
- Cited by
- 930
- References
- 3
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bard, J. F. (1998). Practical Bilevel Optimization. Nonconvex Optimization and Its Applications. https://doi.org/10.1007/978-1-4757-2836-1
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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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