When.com Web Search

Search results

  1. Results From The WOW.Com Content Network
  2. Positive operator - Wikipedia

    en.wikipedia.org/wiki/Positive_operator

    In mathematics (specifically linear algebra, operator theory, and functional analysis) as well as physics, a linear operator acting on an inner product space is called positive-semidefinite (or non-negative) if, for every ⁡ (), , and , , where ⁡ is the domain of .

  3. Positive semidefinite - Wikipedia

    en.wikipedia.org/wiki/Positive_semidefinite

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Pages for logged out editors learn more

  4. 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.

  5. Definite matrix - Wikipedia

    en.wikipedia.org/wiki/Definite_matrix

    In mathematics, a symmetric matrix with real entries is positive-definite if the real number is positive for every nonzero real column vector, where is the row vector transpose of . [1] More generally, a Hermitian matrix (that is, a complex matrix equal to its conjugate transpose) is positive-definite if the real number is positive for every nonzero complex column vector , where denotes the ...

  6. 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 ...

  7. Semidefinite programming - Wikipedia

    en.wikipedia.org/wiki/Semidefinite_programming

    Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function (a user-specified function that the user wants to minimize or maximize) over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron.

  8. Matrix decomposition - Wikipedia

    en.wikipedia.org/wiki/Matrix_decomposition

    Uniqueness: is always unique and equal to (which is always hermitian and positive semidefinite). If A {\displaystyle A} is invertible, then U {\displaystyle U} is unique. Comment: Since any Hermitian matrix admits a spectral decomposition with a unitary matrix, P {\displaystyle P} can be written as P = V D V ∗ {\displaystyle P=VDV^{*}} .

  9. Positive form - Wikipedia

    en.wikipedia.org/wiki/Positive_form

    A form is called strongly positive if it is a linear combination of products of semi-positive forms, with positive real coefficients. A real (p, p) -form η {\displaystyle \eta } on an n -dimensional complex manifold M is called weakly positive if for all strongly positive (n-p, n-p) -forms ζ with compact support, we have ∫ M η ∧ ζ ≥ 0 ...