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  2. Negative definiteness - Wikipedia

    en.wikipedia.org/wiki/Negative_definiteness

    In mathematics, negative definiteness is a property of any object to which a bilinear form may be naturally associated, which is negative-definite. See, in particular: Negative-definite bilinear form; Negative-definite quadratic form; Negative-definite matrix; Negative-definite function

  3. Definite quadratic form - Wikipedia

    en.wikipedia.org/wiki/Definite_quadratic_form

    In mathematics, a definite quadratic form is a quadratic form over some real vector space V that has the same sign (always positive or always negative) for every non-zero vector of V. According to that sign, the quadratic form is called positive-definite or negative-definite.

  4. 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 ...

  5. Positive-definite function - Wikipedia

    en.wikipedia.org/wiki/Positive-definite_function

    A function is negative semi-definite if the inequality is reversed. A function is definite if the weak inequality is replaced with a strong (<, > 0). Examples

  6. Haagerup property - Wikipedia

    en.wikipedia.org/wiki/Haagerup_property

    There is a proper continuous conditionally negative definite function: +. G {\displaystyle G} has the Haagerup approximation property , also known as Property C 0 {\displaystyle C_{0}} : there is a sequence of normalized continuous positive-definite functions ϕ n {\displaystyle \phi _{n}} which vanish at infinity on G {\displaystyle G} and ...

  7. Hessian matrix - Wikipedia

    en.wikipedia.org/wiki/Hessian_matrix

    The above rules stating that extrema are characterized (among critical points with a non-singular Hessian) by a positive-definite or negative-definite Hessian cannot apply here since a bordered Hessian can neither be negative-definite nor positive-definite, as = if is any vector whose sole non-zero entry is its first.

  8. How the Fed and Trump could collide in 2025 [Video]

    www.aol.com/finance/fed-trump-could-collide-2025...

    President-elect Donald Trump and Federal Reserve Chair Jay Powell have clashed before, and there is a chance they will do so again in 2025.. Their collision could unfold in multiple ways. If Trump ...

  9. Second partial derivative test - Wikipedia

    en.wikipedia.org/wiki/Second_partial_derivative_test

    At the remaining critical point (0, 0) the second derivative test is insufficient, and one must use higher order tests or other tools to determine the behavior of the function at this point. (In fact, one can show that f takes both positive and negative values in small neighborhoods around (0, 0) and so this point is a saddle point of f.)