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
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