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  2. Taylor's theorem - Wikipedia

    en.wikipedia.org/wiki/Taylor's_theorem

    If the function f : R n → R is k + 1 times continuously differentiable in a closed ball = {: ‖ ‖} for some >, then one can derive an exact formula for the remainder in terms of (k+1)-th order partial derivatives of f in this neighborhood. [15]

  3. General Leibniz rule - Wikipedia

    en.wikipedia.org/wiki/General_Leibniz_rule

    This formula can be used to derive a formula that computes the symbol of the composition of differential operators. In fact, let P and Q be differential operators (with coefficients that are differentiable sufficiently many times) and R = P ∘ Q . {\displaystyle R=P\circ Q.}

  4. Leibniz integral rule - Wikipedia

    en.wikipedia.org/wiki/Leibniz_integral_rule

    With those tools, the Leibniz integral rule in n dimensions is [4] = () + + ˙, where Ω(t) is a time-varying domain of integration, ω is a p-form, = is the vector field of the velocity, denotes the interior product with , d x ω is the exterior derivative of ω with respect to the space variables only and ˙ is the time derivative of ω. The ...

  5. Rolle's theorem - Wikipedia

    en.wikipedia.org/wiki/Rolle's_theorem

    the function f is n − 1 times continuously differentiable on the closed interval [a, b] and the n th derivative exists on the open interval (a, b), and; there are n intervals given by a 1 < b 1 ≤ a 2 < b 2 ≤ ⋯ ≤ a n < b n in [a, b] such that f (a k) = f (b k) for every k from 1 to n. Then there is a number c in (a, b) such that the n ...

  6. Mean value theorem (divided differences) - Wikipedia

    en.wikipedia.org/wiki/Mean_value_theorem...

    For any n + 1 pairwise distinct points x 0, ..., x n in the domain of an n-times differentiable function f there exists an interior point

  7. Differentiation rules - Wikipedia

    en.wikipedia.org/wiki/Differentiation_rules

    The slope of the constant function is 0, because the tangent line to the constant function is horizontal and its angle is 0. In other words, the value of the constant function, y {\textstyle y} , will not change as the value of x {\textstyle x} increases or decreases.

  8. Newton's method in optimization - Wikipedia

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

    The geometric interpretation of Newton's method is that at each iteration, it amounts to the fitting of a parabola to the graph of () at the trial value , having the same slope and curvature as the graph at that point, and then proceeding to the maximum or minimum of that parabola (in higher dimensions, this may also be a saddle point), see below.

  9. Exact differential equation - Wikipedia

    en.wikipedia.org/wiki/Exact_differential_equation

    Implicit differentiation of the exact second-order equation times will yield an (+) th-order differential equation with new conditions for exactness that can be readily deduced from the form of the equation produced. For example, differentiating the above second-order differential equation once to yield a third-order exact equation gives the ...