Abstract
Basic theoretical results of smooth optimization can be characterized in the image space (e.g., Giannessi, 1984, 1987, 1989) or in an invariant form by using Riemannian geometry and tensor calculus (e.g., Rapcsák, 1989, 1991/b, Rapcsák and Csendes, 1993). An advantage of the latter approach is that the results do not depend on nonlinear coordinate transformations. Thus, theoretical results and numerical representations can be separated. However, we want to emphasize the importance of the good numerical representations of optimization problems which may lead to more efficient methods.
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© 1997 Springer Science+Business Media Dordrecht
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Rapcsák, T. (1997). Nonlinear Coordinate Representations. 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_8
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DOI: https://doi.org/10.1007/978-1-4615-6357-0_8
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7920-1
Online ISBN: 978-1-4615-6357-0
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