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  2. Orthonormal basis - Wikipedia

    en.wikipedia.org/wiki/Orthonormal_basis

    In other words, the space of orthonormal bases is like the orthogonal group, but without a choice of base point: given the space of orthonormal bases, there is no natural choice of orthonormal basis, but once one is given one, there is a one-to-one correspondence between bases and the orthogonal group.

  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 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.

  5. 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 .

  6. Orthogonal basis - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_basis

    The concept of orthogonality may be extended to a vector space over any field of characteristic not 2 equipped with a quadratic form ⁠ ⁠.Starting from the observation that, when the characteristic of the underlying field is not 2, the associated symmetric bilinear form , = ((+) ()) allows vectors and to be defined as being orthogonal with respect to when ⁠ (+) () = ⁠.

  7. Gram–Schmidt process - Wikipedia

    en.wikipedia.org/wiki/Gram–Schmidt_process

    The first two steps of the Gram–Schmidt process. In mathematics, particularly linear algebra and numerical analysis, the Gram–Schmidt process or Gram-Schmidt algorithm is a way of finding a set of two or more vectors that are perpendicular to each other.

  8. Orthogonal group - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_group

    In other words, the space of orthonormal bases is like the orthogonal group, but without a choice of base point: given an orthogonal space, there is no natural choice of orthonormal basis, but once one is given one, there is a one-to-one correspondence between bases and the orthogonal group.

  9. Schauder basis - Wikipedia

    en.wikipedia.org/wiki/Schauder_basis

    The space ℓ ∞ is not separable, and therefore has no Schauder basis. Every orthonormal basis in a separable Hilbert space is a Schauder basis. Every countable orthonormal basis is equivalent to the standard unit vector basis in ℓ 2. The Haar system is an example of a basis for L p ([0, 1]), when 1 ≤ p < ∞. [2]