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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書目名稱Best Approximation in Inner Product Spaces影響因子(影響力)




書目名稱Best Approximation in Inner Product Spaces影響因子(影響力)學科排名




書目名稱Best Approximation in Inner Product Spaces網(wǎng)絡公開度




書目名稱Best Approximation in Inner Product Spaces網(wǎng)絡公開度學科排名




書目名稱Best Approximation in Inner Product Spaces被引頻次




書目名稱Best Approximation in Inner Product Spaces被引頻次學科排名




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書目名稱Best Approximation in Inner Product Spaces年度引用學科排名




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書目名稱Best Approximation in Inner Product Spaces讀者反饋學科排名

sacrum

Existence and Uniqueness of Best Approximations,ce theorems of interest. In particular, the two most useful existence and uniqueness theorems can be deduced from it. They are: (1) Every finite-dimensional subspace is Chebyshev, and (2) every closed convex subset of a Hilbert space is Chebyshev.

名字的誤用

Characterization of Best Approximations,deed, it will be the basis for . characterization theorem that we give. The notion of a dual cone plays an essential role in this characterization. In the particular case where the convex set is a subspace, we obtain the familiar orthogonality condition, which for finite-dimensional subspaces reduce

Malleable

小說

Bounded Linear Functionals and Best Approximation from Hyperplanes and Half-Spaces,subspaces, 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

concentrate

Error of Approximation, 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

臭名昭著

無孔

Interpolation and Approximation,terpolation (SAI), simultaneous approximation and norm-preservation (SAN), simultaneous interpolation and norm-preservation (SIN), and simultaneous approximation and interpolation with norm-preservation (SAIN).

craven

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