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  2. Second partial derivative test - Wikipedia

    en.wikipedia.org/wiki/Second_partial_derivative_test

    The following test can be applied at any critical point a for which the Hessian matrix is invertible: If the Hessian is positive definite (equivalently, has all eigenvalues positive) at a, then f attains a local minimum at a. If the Hessian is negative definite (equivalently, has all eigenvalues negative) at a, then f attains a local maximum at a.

  3. Hessian matrix - Wikipedia

    en.wikipedia.org/wiki/Hessian_matrix

    The Hessian matrix plays an important role in Morse theory and catastrophe theory, because its kernel and eigenvalues allow classification of the critical points. [2] [3] [4] The determinant of the Hessian matrix, when evaluated at a critical point of a function, is equal to the Gaussian curvature of the function considered as a manifold. The ...

  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. Newton's method in optimization - Wikipedia

    en.wikipedia.org/wiki/Newton's_method_in...

    There also exist various quasi-Newton methods, where an approximation for the Hessian (or its inverse directly) is built up from changes in the gradient. If the Hessian is close to a non-invertible matrix, the inverted Hessian can be numerically unstable and the solution may diverge. In this case, certain workarounds have been tried in the past ...

  6. Derivative test - Wikipedia

    en.wikipedia.org/wiki/Derivative_test

    For a function of more than one variable, the second-derivative test generalizes to a test based on the eigenvalues of the function's Hessian matrix at the critical point. In particular, assuming that all second-order partial derivatives of f are continuous on a neighbourhood of a critical point x , then if the eigenvalues of the Hessian at x ...

  7. Energy minimization - Wikipedia

    en.wikipedia.org/wiki/Energy_minimization

    As described above, some method such as quantum mechanics can be used to calculate the energy, E(r) , the gradient of the PES, that is, the derivative of the energy with respect to the position of the atoms, ∂E/∂r and the second derivative matrix of the system, ∂∂E/∂r i ∂r j, also known as the Hessian matrix, which describes the curvature of the PES at r.

  8. Nonlinear conjugate gradient method - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_conjugate...

    There, both step direction and length are computed from the gradient as the solution of a linear system of equations, with the coefficient matrix being the exact Hessian matrix (for Newton's method proper) or an estimate thereof (in the quasi-Newton methods, where the observed change in the gradient during the iterations is used to update the ...

  9. Compliance constants - Wikipedia

    en.wikipedia.org/wiki/Compliance_constants

    The authors use an inverted matrix of force constants, i.e., inverted Hessian matrix, originally introduced by W. T. Taylor and K. S. Pitzer. [9] The insight in choosing the inverted matrix is from the realization that not all elements in the Hessian matrix are necessary—and thus redundant—for describing covalent and non-covalent interactions.