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  2. Linear approximation - Wikipedia

    en.wikipedia.org/wiki/Linear_approximation

    Tangent line at (a, f(a)) In mathematics, a linear approximation is an approximation of a general function using a linear function (more precisely, an affine function). They are widely used in the method of finite differences to produce first order methods for solving or approximating solutions to equations.

  3. Newton's method - Wikipedia

    en.wikipedia.org/wiki/Newton's_method

    It is easy to find situations for which Newton's method oscillates endlessly between two distinct values. For example, for Newton's method as applied to a function f to oscillate between 0 and 1, it is only necessary that the tangent line to f at 0 intersects the x-axis at 1 and that the tangent line to f at 1 intersects the x-axis at 0. [19]

  4. Euler method - Wikipedia

    en.wikipedia.org/wiki/Euler_method

    Consider the problem of calculating the shape of an unknown curve which starts at a given point and satisfies a given differential equation. Here, a differential equation can be thought of as a formula by which the slope of the tangent line to the curve can be computed at any point on the curve, once the position of that point has been calculated.

  5. Numerical differentiation - Wikipedia

    en.wikipedia.org/wiki/Numerical_differentiation

    Therefore, the true derivative of f at x is the limit of the value of the difference quotient as the secant lines get closer and closer to being a tangent line: ′ = (+) (). Since immediately substituting 0 for h results in 0 0 {\displaystyle {\frac {0}{0}}} indeterminate form , calculating the derivative directly can be unintuitive.

  6. Numerical methods for ordinary differential equations - Wikipedia

    en.wikipedia.org/wiki/Numerical_methods_for...

    From any point on a curve, you can find an approximation of a nearby point on the curve by moving a short distance along a line tangent to the curve. Starting with the differential equation ( 1 ), we replace the derivative y ′ by the finite difference approximation

  7. Linearization - Wikipedia

    en.wikipedia.org/wiki/Linearization

    Linearizations of a function are lines—usually lines that can be used for purposes of calculation. Linearization is an effective method for approximating the output of a function = at any = based on the value and slope of the function at =, given that () is differentiable on [,] (or [,]) and that is close to .

  8. Arc length - Wikipedia

    en.wikipedia.org/wiki/Arc_length

    The length of the curve is given by the formula = | ′ | where | ′ | is the Euclidean norm of the tangent vector ′ to the curve. To justify this formula, define the arc length as limit of the sum of linear segment lengths for a regular partition of [ a , b ] {\displaystyle [a,b]} as the number of segments approaches infinity.

  9. Calculus of variations - Wikipedia

    en.wikipedia.org/wiki/Calculus_of_Variations

    Then the Euler–Lagrange equation holds as before in the region where < or >, and in fact the path is a straight line there, since the refractive index is constant. At the x = 0 , {\displaystyle x=0,} f {\displaystyle f} must be continuous, but f ′ {\displaystyle f'} may be discontinuous.