Abstract
Discrete and continuous nonconvex programming problems arise in a host of practical applications in the context of production, location-allocation, distribution, economics and game theory, process design, and engineering design situations. Several recent advances have been made in the development of branch-and-cut algorithms for discrete optimization problems and in polyhedral outer-approximation methods for continuous nonconvex programming problems. At the heart of these approaches is a sequence of linear programming problems that drive the solution process. The success of such algorithms is strongly linked to the strength or tightness of the linear programming representations employed.
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© 1999 Springer Science+Business Media Dordrecht
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Sherali, H.D., Adams, W.P. (1999). Introduction. In: A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems. Nonconvex Optimization and Its Applications, vol 31. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-4388-3_1
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DOI: https://doi.org/10.1007/978-1-4757-4388-3_1
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-4808-3
Online ISBN: 978-1-4757-4388-3
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