enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Peres–Horodecki criterion - Wikipedia

    en.wikipedia.org/wiki/Peres–Horodecki_criterion

    As the transposition map preserves eigenvalues, the spectrum of () is the same as the spectrum of , and in particular () must still be positive semidefinite. Thus must also be positive semidefinite. This proves the necessity of the PPT criterion.

  3. Covariance function - Wikipedia

    en.wikipedia.org/wiki/Covariance_function

    A function is a valid covariance function if and only if [2] this variance is non-negative for all possible choices of N and weights w 1, ..., w N. A function with this property is called positive semidefinite .

  4. 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 .

  5. Sylvester's criterion - Wikipedia

    en.wikipedia.org/wiki/Sylvester's_criterion

    In mathematics, Sylvester’s criterion is a necessary and sufficient criterion to determine whether a Hermitian matrix is positive-definite. Sylvester's criterion states that a n × n Hermitian matrix M is positive-definite if and only if all the following matrices have a positive determinant:

  6. Positive semidefinite - Wikipedia

    en.wikipedia.org/wiki/Positive_semidefinite

    Current events; Random article; About Wikipedia; Contact us; Contribute Help; ... In mathematics, positive semidefinite may refer to: Positive semidefinite function;

  7. Conjugate gradient method - Wikipedia

    en.wikipedia.org/wiki/Conjugate_gradient_method

    The conjugate gradient method can be applied to an arbitrary n-by-m matrix by applying it to normal equations A T A and right-hand side vector A T b, since A T A is a symmetric positive-semidefinite matrix for any A. The result is conjugate gradient on the normal equations (CGN or CGNR). A T Ax = A T b

  8. Positive-definite function - Wikipedia

    en.wikipedia.org/wiki/Positive-definite_function

    One can define positive-definite functions on any locally compact abelian topological group; Bochner's theorem extends to this context. Positive-definite functions on groups occur naturally in the representation theory of groups on Hilbert spaces (i.e. the theory of unitary representations).

  9. Hessian matrix - Wikipedia

    en.wikipedia.org/wiki/Hessian_matrix

    This implies that at a local minimum the Hessian is positive-semidefinite, and at a local maximum the Hessian is negative-semidefinite. For positive-semidefinite and negative-semidefinite Hessians the test is inconclusive (a critical point where the Hessian is semidefinite but not definite may be a local extremum or a saddle point).