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  2. Symmetry of second derivatives - Wikipedia

    en.wikipedia.org/wiki/Symmetry_of_second_derivatives

    The derivative of an integrable function can always be defined as a distribution, and symmetry of mixed partial derivatives always holds as an equality of distributions. The use of formal integration by parts to define differentiation of distributions puts the symmetry question back onto the test functions , which are smooth and certainly ...

  3. Partial derivative - Wikipedia

    en.wikipedia.org/wiki/Partial_derivative

    If the direction of derivative is not repeated, it is called a mixed partial derivative. If all mixed second order partial derivatives are continuous at a point (or on a set), f is termed a C 2 function at that point (or on that set); in this case, the partial derivatives can be exchanged by Clairaut's theorem:

  4. Geometric calculus - Wikipedia

    en.wikipedia.org/wiki/Geometric_calculus

    Let be an -grade multivector.Then we can define an additional pair of operators, the interior and exterior derivatives, = =, = + =. In particular, if is grade 1 (vector-valued function), then we can write

  5. Hessian matrix - Wikipedia

    en.wikipedia.org/wiki/Hessian_matrix

    If all second-order partial derivatives of exist, then the Hessian matrix of is a square matrix, usually defined and arranged as = []. That is, the entry of the i th row and the j th column is ( H f ) i , j = ∂ 2 f ∂ x i ∂ x j . {\displaystyle (\mathbf {H} _{f})_{i,j}={\frac {\partial ^{2}f}{\partial x_{i}\,\partial x_{j}}}.}

  6. Partial differential equation - Wikipedia

    en.wikipedia.org/wiki/Partial_differential_equation

    In mathematics, a partial differential equation (PDE) is an equation which involves a multivariable function and one or more of its partial derivatives.. The function is often thought of as an "unknown" that solves the equation, similar to how x is thought of as an unknown number solving, e.g., an algebraic equation like x 2 − 3x + 2 = 0.

  7. Differential calculus - Wikipedia

    en.wikipedia.org/wiki/Differential_calculus

    The second derivative test can still be used to analyse critical points by considering the eigenvalues of the Hessian matrix of second partial derivatives of the function at the critical point. If all of the eigenvalues are positive, then the point is a local minimum; if all are negative, it is a local maximum.

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