Titlebook: Best Approximation in Inner Product Spaces; Frank Deutsch Textbook 2001 Springer-Verlag New York 2001 Convexity.Hilbert space.algorithms.c

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Nanlin Xu,Ming Ronnier Luo,Xinchao Qusubspaces, these functionals are the most important linear mappings that arise in our work. We saw in the last chapter that every element of the inner product space . naturally generates a bounded linear functional on . (see Theorem 5.18). Here we give a general representation theorem for . bounded

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Hui Fan,Ming Ronnier Luo,Xinchao Qu given an explicit formula for the distance . in the last chapter (Theorem 6.25), and a strengthening of this distance formula in the particular case where the convex set . is either a convex cone or a subspace (Theorem 6.26). Now we will extract still further refinements, improvements, and applicat

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https://doi.org/10.1007/978-3-319-45699-7terpolation (SAI), simultaneous approximation and norm-preservation (SAN), simultaneous interpolation and norm-preservation (SIN), and simultaneous approximation and interpolation with norm-preservation (SAIN).

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978-1-4419-2890-0Springer-Verlag New York 2001

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