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  2. Orthonormality - Wikipedia

    en.wikipedia.org/wiki/Orthonormality

    A unit vector means that the vector has a length of 1, which is also known as normalized. Orthogonal means that the vectors are all perpendicular to each other. A set of vectors form an orthonormal set if all vectors in the set are mutually orthogonal and all of unit length. An orthonormal set which forms a basis is called an orthonormal basis.

  3. Gram–Schmidt process - Wikipedia

    en.wikipedia.org/wiki/Gram–Schmidt_process

    If the Gram–Schmidt process is applied to a linearly dependent sequence, it outputs the 0 vector on the th step, assuming that is a linear combination of , …,. 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.

  4. Orthogonal matrix - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_matrix

    The last column can be fixed to any unit vector, and each choice gives a different copy of O(n) in O(n + 1); in this way O(n + 1) is a bundle over the unit sphere S n with fiber O(n). Similarly, SO( n ) is a subgroup of SO( n + 1) ; and any special orthogonal matrix can be generated by Givens plane rotations using an analogous procedure.

  5. Orthogonalization - Wikipedia

    en.wikipedia.org/wiki/Orthogonalization

    In linear algebra, orthogonalization is the process of finding a set of orthogonal vectors that span a particular subspace.Formally, starting with a linearly independent set of vectors {v 1, ... , v k} in an inner product space (most commonly the Euclidean space R n), orthogonalization results in a set of orthogonal vectors {u 1, ... , u k} that generate the same subspace as the vectors v 1 ...

  6. Orthonormal basis - Wikipedia

    en.wikipedia.org/wiki/Orthonormal_basis

    Using Zorn's lemma and the Gram–Schmidt process (or more simply well-ordering and transfinite recursion), one can show that every Hilbert space admits an orthonormal basis; [7] furthermore, any two orthonormal bases of the same space have the same cardinality (this can be proven in a manner akin to that of the proof of the usual dimension ...

  7. QR decomposition - Wikipedia

    en.wikipedia.org/wiki/QR_decomposition

    A Householder reflection (or Householder transformation) is a transformation that takes a vector and reflects it about some plane or hyperplane. We can use this operation to calculate the QR factorization of an m-by-n matrix with m ≥ n. Q can be used to reflect a vector in such a way that all coordinates but one disappear.

  8. 10 Tips for Seniors to Get Better Sleep This Year - AOL

    www.aol.com/10-tips-seniors-better-sleep...

    Making It Enjoyable and Sustainable. As we age, a good night’s sleep becomes even more crucial for our overall health and well-being. However, maintaining consistent sleep patterns can become a ...

  9. Change of basis - Wikipedia

    en.wikipedia.org/wiki/Change_of_basis

    Normally, a matrix represents a linear map, and the product of a matrix and a column vector represents the function application of the corresponding linear map to the vector whose coordinates form the column vector. The change-of-basis formula is a specific case of this general principle, although this is not immediately clear from its ...