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  2. 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 ...

  3. Hilbert's seventeenth problem - Wikipedia

    en.wikipedia.org/wiki/Hilbert's_seventeenth_problem

    A result of Albrecht Pfister [8] shows that a positive semidefinite form in n variables can be expressed as a sum of 2 n squares. [9] Dubois showed in 1967 that the answer is negative in general for ordered fields. [10] In this case one can say that a positive polynomial is a sum of weighted squares of rational functions with positive ...

  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. Positive semidefinite - Wikipedia

    en.wikipedia.org/wiki/Positive_semidefinite

    In mathematics, positive semidefinite may refer to: Positive semidefinite function; Positive semidefinite matrix; Positive semidefinite quadratic form;

  6. Sylvester's criterion - Wikipedia

    en.wikipedia.org/wiki/Sylvester's_criterion

    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: the upper left 1-by-1 corner of M, the upper left 2-by-2 corner of M, the upper left 3-by-3 corner of M, M itself.

  7. Fidelity of quantum states - Wikipedia

    en.wikipedia.org/wiki/Fidelity_of_quantum_states

    The fidelity between two quantum states and , expressed as density matrices, is commonly defined as: [1] [2] (,) = (⁡).The square roots in this expression are well-defined because both and are positive semidefinite matrices, and the square root of a positive semidefinite matrix is defined via the spectral theorem.

  8. NYT ‘Connections’ Hints and Answers Today, Sunday, December 15

    www.aol.com/nyt-connections-hints-answers-today...

    Get ready for all of today's NYT 'Connections’ hints and answers for #553 on Sunday, December 15, 2024. Today's NYT Connections puzzle for Sunday, December 15, 2024 The New York Times

  9. Second-order cone programming - Wikipedia

    en.wikipedia.org/wiki/Second-order_cone_programming

    Semidefinite programming subsumes SOCPs as the SOCP constraints can be written as linear matrix inequalities (LMI) and can be reformulated as an instance of semidefinite program. [4] The converse, however, is not valid: there are positive semidefinite cones that do not admit any second-order cone representation. [ 3 ]