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  2. Positive-definite function - Wikipedia

    en.wikipedia.org/wiki/Positive-definite_function

    Positive-definiteness arises naturally in the theory of the Fourier transform; it can be seen directly that to be positive-definite it is sufficient for f to be the Fourier transform of a function g on the real line with g(y) ≥ 0.

  3. Toeplitz matrix - Wikipedia

    en.wikipedia.org/wiki/Toeplitz_matrix

    A matrix equation of the form = is called a Toeplitz system if is a Toeplitz matrix. If is an Toeplitz matrix, then the system has at most only unique values, rather than . We might therefore expect that the solution of a Toeplitz system would be easier, and indeed that is the case.

  4. Positive-definite kernel - Wikipedia

    en.wikipedia.org/wiki/Positive-definite_kernel

    In operator theory, a branch of mathematics, a positive-definite kernel is a generalization of a positive-definite function or a positive-definite matrix. It was first introduced by James Mercer in the early 20th century, in the context of solving integral operator equations. Since then, positive-definite functions and their various analogues ...

  5. Positive definiteness - Wikipedia

    en.wikipedia.org/wiki/Positive_definiteness

    In mathematics, positive definiteness is a property of any object to which a bilinear form or a sesquilinear form may be naturally associated, which is positive-definite. See, in particular: Positive-definite bilinear form; Positive-definite function; Positive-definite function on a group; Positive-definite functional; Positive-definite kernel

  6. Cholesky decomposition - Wikipedia

    en.wikipedia.org/wiki/Cholesky_decomposition

    In linear algebra, the Cholesky decomposition or Cholesky factorization (pronounced / ʃ ə ˈ l ɛ s k i / shə-LES-kee) is a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions, e.g., Monte Carlo simulations.

  7. Lyapunov equation - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_equation

    In particular, the discrete-time Lyapunov equation (also known as Stein equation) for is A X A H − X + Q = 0 {\displaystyle AXA^{H}-X+Q=0} where Q {\displaystyle Q} is a Hermitian matrix and A H {\displaystyle A^{H}} is the conjugate transpose of A {\displaystyle A} , while the continuous-time Lyapunov equation is

  8. Broyden–Fletcher–Goldfarb–Shanno algorithm - Wikipedia

    en.wikipedia.org/wiki/Broyden–Fletcher...

    The curvature condition > should be satisfied for + to be positive definite, which can be verified by pre-multiplying the secant equation with . If the function is not strongly convex , then the condition has to be enforced explicitly e.g. by finding a point x k +1 satisfying the Wolfe conditions , which entail the curvature condition, using ...

  9. Mercer's theorem - Wikipedia

    en.wikipedia.org/wiki/Mercer's_theorem

    In mathematics, specifically functional analysis, Mercer's theorem is a representation of a symmetric positive-definite function on a square as a sum of a convergent sequence of product functions. This theorem, presented in ( Mercer 1909 ), is one of the most notable results of the work of James Mercer (1883–1932).

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