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  2. 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 ...

  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. 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 Q T Q = Q Q T = I , {\displaystyle Q^{\mathrm {T} }Q=QQ^{\mathrm {T} }=I,} where Q T is the transpose of Q and I is the identity matrix .

  5. 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.

  6. 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.

  7. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    The geometric content of the SVD theorem can thus be summarized as follows: for every linear map ⁠: ⁠ one can find orthonormal bases of ⁠ ⁠ and ⁠ ⁠ such that ⁠ ⁠ maps the ⁠ ⁠-th basis vector of ⁠ ⁠ to a non-negative multiple of the ⁠ ⁠-th basis vector of ⁠, ⁠ and sends the leftover basis vectors to zero.

  8. 3D rotation group - Wikipedia

    en.wikipedia.org/wiki/3D_rotation_group

    Every non-trivial rotation is determined by its axis of rotation (a line through the origin) and its angle of rotation. Rotations are not commutative (for example, rotating R 90° in the x-y plane followed by S 90° in the y-z plane is not the same as S followed by R), making the 3D rotation group a nonabelian group.

  9. 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.