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  2. Second partial derivative test - Wikipedia

    en.wikipedia.org/wiki/Second_partial_derivative_test

    Note that in the one-variable case, the Hessian condition simply gives the usual second derivative test. In the two variable case, (,) and (,) are the principal minors of the Hessian. The first two conditions listed above on the signs of these minors are the conditions for the positive or negative definiteness of the Hessian.

  3. Hessian matrix - Wikipedia

    en.wikipedia.org/wiki/Hessian_matrix

    The second-derivative test for functions of one and two variables is simpler than the general case. In one variable, the Hessian contains exactly one second derivative; if it is positive, then is a local minimum, and if it is negative, then is a local

  4. 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 ...

  5. 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 ...

  6. 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.

  7. Hessian automatic differentiation - Wikipedia

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

    The graph colouring techniques explore sparsity patterns of the Hessian matrix and cheap Hessian vector products to obtain the entire matrix. Thus these techniques are suited for large, sparse matrices. The general strategy of any such colouring technique is as follows. Obtain the global sparsity pattern of

  8. 6 Big Shakeups Coming to Social Security in 2025 - AOL

    www.aol.com/6-big-shakeups-coming-social...

    In 2025, the so-called income test limit rises from $22,320 to $23,400. After that, the SSA deducts $1 of benefits for every $2 earned over that income threshold. For those who will reach full ...

  9. Newton's method in optimization - Wikipedia

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

    The above iterative scheme can be generalized to > dimensions by replacing the derivative with the gradient (different authors use different notation for the gradient, including ′ = = ()), and the reciprocal of the second derivative with the inverse of the Hessian matrix (different authors use different notation for the Hessian, including ...