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  2. Notation for differentiation - Wikipedia

    en.wikipedia.org/wiki/Notation_for_differentiation

    It is particularly common when the equation y = f(x) is regarded as a functional relationship between dependent and independent variables y and x. Leibniz's notation makes this relationship explicit by writing the derivative as: [ 1 ] d y d x . {\displaystyle {\frac {dy}{dx}}.}

  3. Derivative - Wikipedia

    en.wikipedia.org/wiki/Derivative

    In mathematics, the derivative is a fundamental tool that quantifies the sensitivity to change of a function's output with respect to its input. The derivative of a function of a single variable at a chosen input value, when it exists, is the slope of the tangent line to the graph of the function at that point.

  4. Differentiation rules - Wikipedia

    en.wikipedia.org/wiki/Differentiation_rules

    The derivative of the function at a point is the slope of the line tangent to the curve at the point. Slope of the constant function is zero, because the tangent line to the constant function is horizontal and its angle is zero. In other words, the value of the constant function, y, will not change as the value of x increases or decreases.

  5. Chain rule - Wikipedia

    en.wikipedia.org/wiki/Chain_rule

    In calculus, the chain rule is a formula that expresses the derivative of the composition of two differentiable functions f and g in terms of the derivatives of f and g.More precisely, if = is the function such that () = (()) for every x, then the chain rule is, in Lagrange's notation, ′ = ′ (()) ′ (). or, equivalently, ′ = ′ = (′) ′.

  6. Numerical differentiation - Wikipedia

    en.wikipedia.org/wiki/Numerical_differentiation

    A simple two-point estimation is to compute the slope of a nearby secant line through the points (x, f(x)) and (x + h, f(x + h)). [1] Choosing a small number h, h represents a small change in x, and it can be either positive or negative. The slope of this line is (+) ().

  7. Generalizations of the derivative - Wikipedia

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

    Of course, the Jacobian matrix of the composition g ° f is a product of corresponding Jacobian matrices: J x (g ° f) =J ƒ(x) (g)J x (ƒ). This is a higher-dimensional statement of the chain rule. For real valued functions from R n to R (scalar fields), the Fréchet derivative corresponds to a vector field called the total derivative.

  8. Leibniz's notation - Wikipedia

    en.wikipedia.org/wiki/Leibniz's_notation

    [1] Consider y as a function of a variable x, or y = f(x). If this is the case, then the derivative of y with respect to x, which later came to be viewed as the limit = (+) (), was, according to Leibniz, the quotient of an infinitesimal increment of y by an infinitesimal increment of x, or = ′ (), where the right hand side is Joseph-Louis ...

  9. Polynomial - Wikipedia

    en.wikipedia.org/wiki/Polynomial

    f(x) = a 0 + a 1 x + a 2 x 2 + ⋯ + a n x n, where a n ≠ 0 and n ≥ 2 is a continuous non-linear curve. A non-constant polynomial function tends to infinity when the variable increases indefinitely (in absolute value ).