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Polynomial Variable Metric Methods For Linear Optimization

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Book cover Smooth Nonlinear Optimization in R

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 19))

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Abstract

Since the elaboration of the framework by Karmarkar, many interior point algorithms have been proposed for linear optimization. Although these variants can be classified into main categories, e.g.: (i) projective methods, (ii) “pure” affine-scaling methods, (iii) path-following methods, (iv) affine potential reduction methods, a different variant needs a different investigation of its convergence or polynomial status. Thus, there is a natural question: how should we analyze the behaviour of these algorithms? A good survey was published on interior point methods (Terlaky (ed.), 1996).

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© 1997 Springer Science+Business Media Dordrecht

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Rapcsák, T. (1997). Polynomial Variable Metric Methods For Linear Optimization. In: Smooth Nonlinear Optimization in R n . Nonconvex Optimization and Its Applications, vol 19. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6357-0_12

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  • DOI: https://doi.org/10.1007/978-1-4615-6357-0_12

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7920-1

  • Online ISBN: 978-1-4615-6357-0

  • eBook Packages: Springer Book Archive

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