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  2. Difference quotient - Wikipedia

    en.wikipedia.org/wiki/Difference_quotient

    Difference quotients may also find relevance in applications involving Time discretization, where the width of the time step is used for the value of h. The difference quotient is sometimes also called the Newton quotient [10] [12] [13] [14] (after Isaac Newton) or Fermat's difference quotient (after Pierre de Fermat). [15]

  3. Discrete calculus - Wikipedia

    en.wikipedia.org/wiki/Discrete_calculus

    The process of finding the difference quotient is called differentiation. Given a function defined at several points of the real line, the difference quotient at that point is a way of encoding the small-scale (i.e., from the point to the next) behavior of the function.

  4. Symmetric derivative - Wikipedia

    en.wikipedia.org/wiki/Symmetric_derivative

    For differentiable functions, the symmetric difference quotient does provide a better numerical approximation of the derivative than the usual difference quotient. [3] The symmetric derivative at a given point equals the arithmetic mean of the left and right derivatives at that point, if the latter two both exist. [1] [2]: 6

  5. Numerical differentiation - Wikipedia

    en.wikipedia.org/wiki/Numerical_differentiation

    The simplest method is to use finite difference approximations. 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.

  6. Finite difference - Wikipedia

    en.wikipedia.org/wiki/Finite_difference

    An infinite difference is a further generalization, where the finite sum above is replaced by an infinite series. Another way of generalization is making coefficients μ k depend on point x: μ k = μ k (x), thus considering weighted finite difference. Also one may make the step h depend on point x: h = h(x).

  7. Chain rule - Wikipedia

    en.wikipedia.org/wiki/Chain_rule

    The latter is the difference quotient for g at a, and because g is differentiable at a by assumption, its limit as x tends to a exists and equals g′(a). As for Q(g(x)), notice that Q is defined wherever f is. Furthermore, f is differentiable at g(a) by assumption, so Q is continuous at g(a), by definition of the derivative.

  8. 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.

  9. Tangent - Wikipedia

    en.wikipedia.org/wiki/Tangent

    The slope of the secant line passing through p and q is equal to the difference quotient (+) (). As the point q approaches p, which corresponds to making h smaller and smaller, the difference quotient should approach a certain limiting value k, which is the slope of the tangent line at the point p.