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  2. Linearity of differentiation - Wikipedia

    en.wikipedia.org/wiki/Linearity_of_differentiation

    One could also define both the second constant coefficient and the second function to be 0, where the domain of the second function is a superset of the first function, among other possibilities.) On the contrary, if we first prove the constant factor rule and the sum rule, we can prove linearity and the difference rule.

  3. Second derivative - Wikipedia

    en.wikipedia.org/wiki/Second_derivative

    The second derivative of a function f can be used to determine the concavity of the graph of f. [2] A function whose second derivative is positive is said to be concave up (also referred to as convex), meaning that the tangent line near the point where it touches the function will lie below the graph of the function.

  4. Parametric derivative - Wikipedia

    en.wikipedia.org/wiki/Parametric_derivative

    In calculus, a parametric derivative is a derivative of a dependent variable with respect to another dependent variable that is taken when both variables depend on an independent third variable, usually thought of as "time" (that is, when the dependent variables are x and y and are given by parametric equations in t).

  5. Proofs involving ordinary least squares - Wikipedia

    en.wikipedia.org/wiki/Proofs_involving_ordinary...

    The normal equations can be derived directly from a matrix representation of the problem as follows. The objective is to minimize = ‖ ‖ = () = +.Here () = has the dimension 1x1 (the number of columns of ), so it is a scalar and equal to its own transpose, hence = and the quantity to minimize becomes

  6. Jacobian matrix and determinant - Wikipedia

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

    [a] This means that the function that maps y to f(x) + J(x) ⋅ (y – x) is the best linear approximation of f(y) for all points y close to x. The linear map h → J(x) ⋅ h is known as the derivative or the differential of f at x. When m = n, the Jacobian matrix is square, so its determinant is a well-defined function of x, known as the ...

  7. Symmetry of second derivatives - Wikipedia

    en.wikipedia.org/wiki/Symmetry_of_second_derivatives

    The symmetry may be broken if the function fails to have differentiable partial derivatives, which is possible if Clairaut's theorem is not satisfied (the second partial derivatives are not continuous). The function f(x, y), as shown in equation , does not have symmetric second derivatives at its origin.

  8. Leibniz's notation - Wikipedia

    en.wikipedia.org/wiki/Leibniz's_notation

    However, Leibniz did use his d notation as we would today use operators, namely he would write a second derivative as ddy and a third derivative as dddy. In 1695 Leibniz started to write d 2 ⋅x and d 3 ⋅x for ddx and dddx respectively, but l'Hôpital, in his textbook on calculus written around the same time, used Leibniz's original forms. [18]

  9. Method of characteristics - Wikipedia

    en.wikipedia.org/wiki/Method_of_characteristics

    For this PDE to be linear, the coefficients a i may be functions of the spatial variables only, and independent of u. For it to be quasilinear, [6] a i may also depend on the value of the function, but not on any derivatives. The distinction between these two cases is inessential for the discussion here.