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  2. Orthonormality - Wikipedia

    en.wikipedia.org/wiki/Orthonormality

    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 relationship between the diagonalizability of an operator and how it acts on the orthonormal basis vectors.

  3. Orthonormal basis - Wikipedia

    en.wikipedia.org/wiki/Orthonormal_basis

    [1] [2] [3] For example, the standard basis for a Euclidean space is an orthonormal basis, where the relevant inner product is the dot product of vectors. The image of the standard basis under a rotation or reflection (or any orthogonal transformation ) is also orthonormal, and every orthonormal basis for R n {\displaystyle \mathbb {R} ^{n ...

  4. Orthogonality (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Orthogonality_(mathematics)

    For example, the y-axis is normal to the curve = at the origin. However, normal may also refer to the magnitude of a vector. In particular, a set is called orthonormal (orthogonal plus normal) if it is an orthogonal set of unit vectors. As a result, use of the term normal to mean "orthogonal" is often avoided.

  5. Stiefel manifold - Wikipedia

    en.wikipedia.org/wiki/Stiefel_manifold

    Let stand for ,, or . The Stiefel manifold () can be thought of as a set of n × k matrices by writing a k-frame as a matrix of k column vectors in . The orthonormality condition is expressed by A*A = where A* denotes the conjugate transpose of A and denotes the k × k identity matrix.

  6. Spherical harmonics - Wikipedia

    en.wikipedia.org/wiki/Spherical_harmonics

    For example, as can be seen from the table of spherical harmonics, the usual p functions (=) are complex and mix axis directions, but the real versions are essentially just x, y, and z. Spherical harmonics in Cartesian form

  7. Orthogonal matrix - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_matrix

    In linear algebra, an orthogonal matrix, or orthonormal matrix, is a real square matrix whose columns and rows are orthonormal vectors.. One way to express this is = =, where Q T is the transpose of Q and I is the identity matrix.

  8. Inner product space - Wikipedia

    en.wikipedia.org/wiki/Inner_product_space

    For example, if = with inner product , = ¯, where is a vector space over the field , then = is a vector space over and , is the dot product, where = + = is identified with the point (,) = (and similarly for ); thus the standard inner product , = ¯, on is an "extension" the dot product .

  9. Schur orthogonality relations - Wikipedia

    en.wikipedia.org/wiki/Schur_orthogonality_relations

    The space of complex-valued class functions of a finite group G has a natural inner product: , := | | () ¯ where () ¯ denotes the complex conjugate of the value of on g.With respect to this inner product, the irreducible characters form an orthonormal basis for the space of class functions, and this yields the orthogonality relation for the rows of the character table: