# inner product space

(redirected from Inner product spaces)

## inner product space

[¦in·ər ′präd·əkt ‚spās]
(mathematics)
A vector space that has an inner product defined on it. Also known as generalized Euclidean space; Hermitian space; pre-Hilbert space.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
Ungar, "Group-like structure underlying the unit ball in real inner product spaces," Results in Mathematics, vol.
The other topics are bases and dimension, linear transformations and matrices, elementary matrix operations, diagonalization, canonical forms, and inner product spaces. They include many exercises and emphasize the importance of practice.
As we can see in the fourth section, the Levinson Popoviciu inequality remains valid on the inner product spaces, under its original hypothesis or Mercer results hypothesis.
Calculation of Jordan forms is followed by normed linear spaces, inner product spaces, orthogonality, and symmetric, Hermitian, and normal matrices.
The commentary on Hilbert space entertains numerous illustrations of the inner product spaces wherein the metric produced by the inner product profits a complete metric space.
Guo, Some basic theories of random normed linear spaces and random inner product spaces, Acta Anal.
Among the topics are inner product spaces, discrete Fourier analysis, multi-resolution analysis, and the Daubechies wavelets.
for any x, y [member of] H, then, on making use of the superadditivity properties of the various functionals defined in Section ??, we can state the following refinements of the Schwarz inequality in inner product spaces:
Most (but not all) of the background mathematics required to understand SVM theory (including vector spaces, inner product spaces, Hilbert spaces, and eigenvalues) are presented in an appendix.
The topics include inner product spaces and orthogonality, the Jordan and Weyr canonical forms, unitary similarity and normal matrices, Hermitian matrices, vector and matrix norms, some matrix factorizations, circulant and block cycle matrices, matrices of zeros and ones, Hadamard matrices, directed graphs, non-negative matrices, and linear dynamical systems.
The primary text covers linear equations, matrices, and determinants, Euclidean vector spaces, Rn, and general vector spaces, eigenvalues and eigenvectors, inner product spaces, diagonalization and quadratic forms, general linear transformations, and numerical methods.
The authors (both professors of mathematics at the Rose- Hulman Institute of Technology) develop the mathematical framework of vector and inner product spaces upon which signal and image processing depends; develop traditional Fourier-based transform techniques, primarily in the discrete case, but also somewhat in the continuous setting; and provide entry-level material on filtering, convolution, filter banks, and wavelets.

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