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  2. Gram–Schmidt process - Wikipedia

    en.wikipedia.org/wiki/GramSchmidt_process

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

  3. Frenet–Serret formulas - Wikipedia

    en.wikipedia.org/wiki/Frenet–Serret_formulas

    An alternative way to arrive at the same expressions is to take the first three derivatives of the curve r′(t), r′′(t), r′′′(t), and to apply the Gram-Schmidt process. The resulting ordered orthonormal basis is precisely the TNB frame. This procedure also generalizes to produce Frenet frames in higher dimensions.

  4. Iwasawa decomposition - Wikipedia

    en.wikipedia.org/wiki/Iwasawa_decomposition

    In mathematics, the Iwasawa decomposition (aka KAN from its expression) of a semisimple Lie group generalises the way a square real matrix can be written as a product of an orthogonal matrix and an upper triangular matrix (QR decomposition, a consequence of GramSchmidt orthogonalization).

  5. Orthogonal polynomials - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_polynomials

    In other words, the sequence is obtained from the sequence of monomials 1, x, x 2, … by the GramSchmidt process with respect to this inner product. Usually the sequence is required to be orthonormal , namely, P n , P n = 1 , {\displaystyle \langle P_{n},P_{n}\rangle =1,} however, other normalisations are sometimes used.

  6. Wikipedia : Featured picture candidates/Gram-Schmidt ...

    en.wikipedia.org/wiki/Wikipedia:Featured_picture...

    1 Gram-Schmidt orthonormalization process. Toggle the table of contents. Wikipedia: Featured picture candidates/Gram-Schmidt orthonormalization ... Download QR code ...

  7. Schmidt decomposition - Wikipedia

    en.wikipedia.org/wiki/Schmidt_decomposition

    In linear algebra, the Schmidt decomposition (named after its originator Erhard Schmidt) refers to a particular way of expressing a vector in the tensor product of two inner product spaces. It has numerous applications in quantum information theory , for example in entanglement characterization and in state purification , and plasticity .

  8. File:Gram–Schmidt process.svg - Wikipedia

    en.wikipedia.org/wiki/File:GramSchmidt_process...

    $ tex Gram-Schmidt_process.tex && dvips -E Gram-Schmidt_process.dvi; Outline fonts $ eps2eps -dNOCACHE Gram-Schmidt_process.ps Gram-Schmidt_process2.eps; Fix bounding box $ ps2epsi Gram-Schmidt_process2.eps Gram-Schmidt_process.eps; Convert to Sketch $ pstoedit -f sk Gram-Schmidt_process.eps Gram-Schmidt_process.sk; Convert to SVG

  9. QR decomposition - Wikipedia

    en.wikipedia.org/wiki/QR_decomposition

    More generally, we can factor a complex m×n matrix A, with m ≥ n, as the product of an m×m unitary matrix Q and an m×n upper triangular matrix R.As the bottom (m−n) rows of an m×n upper triangular matrix consist entirely of zeroes, it is often useful to partition R, or both R and Q: