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  2. Orthonormal basis - Wikipedia

    en.wikipedia.org/wiki/Orthonormal_basis

    Given a pre-Hilbert space , an orthonormal basis for is an orthonormal set of vectors with the property that every vector in can be written as an infinite linear combination of the vectors in the basis. In this case, the orthonormal basis is sometimes called a Hilbert basis for . Note that an orthonormal basis in this sense is not generally a ...

  3. Orthonormality - Wikipedia

    en.wikipedia.org/wiki/Orthonormality

    The Gram-Schmidt theorem, together with the axiom of choice, guarantees that every vector space admits an orthonormal basis. This is possibly the most significant use of orthonormality, as this fact permits operators on inner-product spaces to be discussed in terms of their action on the space's orthonormal basis vectors. What results is a deep ...

  4. Hilbert space - Wikipedia

    en.wikipedia.org/wiki/Hilbert_space

    Despite the name, an orthonormal basis is not, in general, a basis in the sense of linear algebra (Hamel basis). More precisely, an orthonormal basis is a Hamel basis if and only if the Hilbert space is a finite-dimensional vector space. [90] Completeness of an orthonormal system of vectors of a Hilbert space can be equivalently restated as:

  5. 3D rotation group - Wikipedia

    en.wikipedia.org/wiki/3D_rotation_group

    With respect to the standard basis e 1, e 2, e 3 of the columns of R are given by (Re 1, Re 2, Re 3). Since the standard basis is orthonormal, and since R preserves angles and length, the columns of R form another orthonormal basis. This orthonormality condition can be expressed in the form

  6. Gram–Schmidt process - Wikipedia

    en.wikipedia.org/wiki/Gram–Schmidt_process

    If an orthonormal basis is to be produced, then the algorithm should test for zero vectors in the output and discard them because no multiple of a zero vector can have a length of 1. The number of vectors output by the algorithm will then be the dimension of the space spanned by the original inputs.

  7. Orthogonal transformation - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_transformation

    In finite-dimensional spaces, the matrix representation (with respect to an orthonormal basis) of an orthogonal transformation is an orthogonal matrix. Its rows are mutually orthogonal vectors with unit norm, so that the rows constitute an orthonormal basis of V. The columns of the matrix form another orthonormal basis of V.

  8. Principal curvature - Wikipedia

    en.wikipedia.org/wiki/Principal_curvature

    For hypersurfaces in higher-dimensional Euclidean spaces, the principal curvatures may be defined in a directly analogous fashion. The principal curvatures are the eigenvalues of the matrix of the second fundamental form (,) in an orthonormal basis of the tangent space. The principal directions are the corresponding eigenvectors.

  9. Spherical harmonics - Wikipedia

    en.wikipedia.org/wiki/Spherical_harmonics

    Plot of the spherical harmonic (,) with = and = and = in the complex plane from to + with colors created with Mathematica 13.1 function ComplexPlot3D In acoustics , [ 8 ] the Laplace spherical harmonics are generally defined as (this is the convention used in this article) Y ℓ m ( θ , φ ) = ( 2 ℓ + 1 ) 4 π ( ℓ − m ) !