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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. Schmidt reaction - Wikipedia

    en.wikipedia.org/wiki/Schmidt_reaction

    The carboxylic acid Schmidt reaction starts with acylium ion 1 obtained from protonation and loss of water. Reaction with hydrazoic acid forms the protonated azido ketone 2 , which goes through a rearrangement reaction with the alkyl group R, migrating over the C-N bond with expulsion of nitrogen.

  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. Generalized minimal residual method - Wikipedia

    en.wikipedia.org/wiki/Generalized_minimal...

    The minimum can be computed using a QR decomposition: find an (n + 1)-by-(n + 1) orthogonal matrix Ω n and an (n + 1)-by-n upper triangular matrix ~ such that ~ = ~. The triangular matrix has one more row than it has columns, so its bottom row consists of zero.

  6. QR decomposition - Wikipedia

    en.wikipedia.org/wiki/QR_decomposition

    where R 1 is an n×n upper triangular matrix, 0 is an (m − n)×n zero matrix, Q 1 is m×n, Q 2 is m×(m − n), and Q 1 and Q 2 both have orthogonal columns. Golub & Van Loan (1996 , §5.2) call Q 1 R 1 the thin QR factorization of A ; Trefethen and Bau call this the reduced QR factorization . [ 1 ]

  7. Arnoldi iteration - Wikipedia

    en.wikipedia.org/wiki/Arnoldi_iteration

    In numerical linear algebra, the Arnoldi iteration is an eigenvalue algorithm and an important example of an iterative method.Arnoldi finds an approximation to the eigenvalues and eigenvectors of general (possibly non-Hermitian) matrices by constructing an orthonormal basis of the Krylov subspace, which makes it particularly useful when dealing with large sparse matrices.

  8. 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.

  9. Gram matrix - Wikipedia

    en.wikipedia.org/wiki/Gram_matrix

    The Gram matrix is symmetric in the case the inner product is real-valued; it is Hermitian in the general, complex case by definition of an inner product. The Gram matrix is positive semidefinite, and every positive semidefinite matrix is the Gramian matrix for some set of vectors. The fact that the Gramian matrix is positive-semidefinite can ...