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Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 31))

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Abstract

In Chapter 7, we discussed the design and theory of a Reformulation-Linearization Technique for solving polynomial programming problems. We now return to this class of problems, but this time, we shall focus on specific implementation issues and describe various algorithmic strategies and additional classes of RLT constraints that can be gainfully employed in enhancing the performance of an RLT-based solution procedure. For the sake of convenience, we restate below the type of polynomial programming problems considered here, as introduced in Chapter 7.

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© 1999 Springer Science+Business Media Dordrecht

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Sherali, H.D., Adams, W.P. (1999). Reformulation-Convexification Technique for Polynomial Programs: Design and Implementation. 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_9

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  • DOI: https://doi.org/10.1007/978-1-4757-4388-3_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4808-3

  • Online ISBN: 978-1-4757-4388-3

  • eBook Packages: Springer Book Archive

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