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  2. Second partial derivative test - Wikipedia

    en.wikipedia.org/wiki/Second_partial_derivative_test

    Thus, the second partial derivative test indicates that f(x, y) has saddle points at (0, −1) and (1, −1) and has a local maximum at (,) since = <. 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.

  3. Hessian matrix - Wikipedia

    en.wikipedia.org/wiki/Hessian_matrix

    In mathematics, the Hessian matrix, Hessian or (less commonly) Hesse matrix is a square matrix of second-order partial derivatives of a scalar-valued function, or scalar field. It describes the local curvature of a function of many variables.

  4. Symmetry of second derivatives - Wikipedia

    en.wikipedia.org/wiki/Symmetry_of_second_derivatives

    In other words, the matrix of the second-order partial derivatives, known as the Hessian matrix, is a symmetric matrix. Sufficient conditions for the symmetry to hold are given by Schwarz's theorem, also called Clairaut's theorem or Young's theorem. [1] [2]

  5. Generalizations of the derivative - Wikipedia

    en.wikipedia.org/wiki/Generalizations_of_the...

    For example, the second order partial derivatives of a scalar function of n variables can be organized into an n by n matrix, the Hessian matrix. One of the subtle points is that the higher derivatives are not intrinsically defined, and depend on the choice of the coordinates in a complicated fashion (in particular, the Hessian matrix of a ...

  6. Jacobian matrix and determinant - Wikipedia

    en.wikipedia.org/wiki/Jacobian_matrix_and...

    Second derivative; Implicit differentiation ... this row vector of all first-order partial derivatives of f is the transpose of the gradient of f, ... the Hessian ...

  7. Partial derivative - Wikipedia

    en.wikipedia.org/wiki/Partial_derivative

    Own and cross partial derivatives appear in the Hessian matrix which is used in the second order conditions in optimization problems. The higher order partial derivatives can be obtained by successive differentiation

  8. Hessian automatic differentiation - Wikipedia

    en.wikipedia.org/wiki/Hessian_automatic...

    See the example figure on the right. Appended to this nonlinear edge is an edge weight that is the second-order partial derivative of the nonlinear node in relation to its predecessors. This nonlinear edge is subsequently pushed down to further predecessors in such a way that when it reaches the independent nodes, its edge weight is the second ...

  9. Notation for differentiation - Wikipedia

    en.wikipedia.org/wiki/Notation_for_differentiation

    Hessian matrix – Matrix of second derivatives Jacobian matrix – Matrix of all first-order partial derivatives of a vector-valued function Pages displaying short descriptions of redirect targets List of mathematical symbols by subject