inner product space

(redirected from Inner products)

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.
References in periodicals archive ?
If the inner products are assumed Euclidean, their computation accounts for a total of 2N x ([d.
According to equation (1), all of the inner products in Table 6 are shown as below:
Sanz-Serna: On polynomials orthogonal with respect to certain Sobolev inner products, J.
The core of the book presents an axiomatic development of the most important elements of finite-dimensional linear algebra: vector spaces, linear operators, norms and inner products, and determinants and eigenvalues.
For the subclass J P (X), of all inner products defined on X, of H(X) and y [not equal to] 0, we may define
However, not all kernels correspond to inner products in some feature space F could be used.
Let us mention that we do not address here the C G method for indefinite systems in so-called non-standard inner products as treated, for instance, in [4, 22, 23, 24].
summability, integral transforms of hypergeometric functions, the constructive theory of approximation, orthogonal polynomials and Sobolev inner products, orthogonal and other polynomials on inverse images of polynomial mappings, and analytic number theory and approximation.
The chapters are grouped into five sections, the first introduces the imaging tasks (direct, inverse, and system analysis), the basic concepts of linear algebra for vectors and functions, including complex-valued vectors, and inner products of vectors and functions.
When comparing the overall performance of the steepest descent and conjugate gradient algorithms, we notice that the influence of choosing the inner products (4.
He explains vector spaces and bases, linear transformations and operators, eigenvalues, circles and ellipses, inner products, adjoints, Hermitian operators, unitary operators, the wave equation, continuous spectre and the Dirac delta function, Fourier transforms, Green's and functions, and includes an appendix on matrix operations (new to this edition) and a full chapter on crucial applications.
The paper convincingly argued that Chebyshevbased methods can give the same accuracy as Krylov techniques using the same polynomial degree, but that they can be much less expensive as they require no inner products.

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