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  2. Array programming - Wikipedia

    en.wikipedia.org/wiki/Array_programming

    Function rank is an important concept to array programming languages in general, by analogy to tensor rank in mathematics: functions that operate on data may be classified by the number of dimensions they act on. Ordinary multiplication, for example, is a scalar ranked function because it operates on zero-dimensional data (individual numbers).

  3. Eigenvalue algorithm - Wikipedia

    en.wikipedia.org/wiki/Eigenvalue_algorithm

    Given an n × n square matrix A of real or complex numbers, an eigenvalue λ and its associated generalized eigenvector v are a pair obeying the relation [1] =,where v is a nonzero n × 1 column vector, I is the n × n identity matrix, k is a positive integer, and both λ and v are allowed to be complex even when A is real.l When k = 1, the vector is called simply an eigenvector, and the pair ...

  4. Vector-valued function - Wikipedia

    en.wikipedia.org/wiki/Vector-valued_function

    A vector-valued function, also referred to as a vector function, is a mathematical function of one or more variables whose range is a set of multidimensional vectors or infinite-dimensional vectors. The input of a vector-valued function could be a scalar or a vector (that is, the dimension of the domain could be 1 or greater than 1); the ...

  5. Inverse iteration - Wikipedia

    en.wikipedia.org/wiki/Inverse_iteration

    In numerical analysis, inverse iteration (also known as the inverse power method) is an iterative eigenvalue algorithm. It allows one to find an approximate eigenvector when an approximation to a corresponding eigenvalue is already known. The method is conceptually similar to the power method. It appears to have originally been developed to ...

  6. QR algorithm - Wikipedia

    en.wikipedia.org/wiki/QR_algorithm

    The vector converges to an eigenvector of the largest eigenvalue. Instead, the QR algorithm works with a complete basis of vectors, using QR decomposition to renormalize (and orthogonalize). For a symmetric matrix A , upon convergence, AQ = QΛ , where Λ is the diagonal matrix of eigenvalues to which A converged, and where Q is a composite of ...

  7. Jacobi eigenvalue algorithm - Wikipedia

    en.wikipedia.org/wiki/Jacobi_eigenvalue_algorithm

    procedure jacobi(S ∈ R n×n; out e ∈ R n; out E ∈ R n×n) var i, k, l, m, state ∈ N s, c, t, p, y, d, r ∈ R ind ∈ N n changed ∈ L n function maxind(k ∈ N) ∈ N ! index of largest off-diagonal element in row k m := k +1 for i := k +2 to n do if │ S ki │ > │ S km │ then m := i endif endfor return m endfunc procedure ...

  8. Power iteration - Wikipedia

    en.wikipedia.org/wiki/Power_iteration

    In mathematics, power iteration (also known as the power method) is an eigenvalue algorithm: given a diagonalizable matrix, the algorithm will produce a number , which is the greatest (in absolute value) eigenvalue of , and a nonzero vector , which is a corresponding eigenvector of , that is, =.

  9. Conjugate gradient method - Wikipedia

    en.wikipedia.org/wiki/Conjugate_gradient_method

    This suggests taking the first basis vector p 0 to be the negative of the gradient of f at x = x 0. The gradient of f equals Ax − b. Starting with an initial guess x 0, this means we take p 0 = b − Ax 0. The other vectors in the basis will be conjugate to the gradient, hence the name conjugate gradient method.