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  2. De Casteljau's algorithm - Wikipedia

    en.wikipedia.org/wiki/De_Casteljau's_algorithm

    Repeat the process until you arrive at the single point – this is the point of the curve corresponding to the parameter . The following picture shows this process for a cubic Bézier curve: Note that the intermediate points that were constructed are in fact the control points for two new Bézier curves, both exactly coincident with the old one.

  3. Fourth, fifth, and sixth derivatives of position - Wikipedia

    en.wikipedia.org/wiki/Fourth,_fifth,_and_sixth...

    Snap, [6] or jounce, [2] is the fourth derivative of the position vector with respect to time, or the rate of change of the jerk with respect to time. [4] Equivalently, it is the second derivative of acceleration or the third derivative of velocity, and is defined by any of the following equivalent expressions: = ȷ = = =.

  4. Numerical differentiation - Wikipedia

    en.wikipedia.org/wiki/Numerical_differentiation

    Download QR code; Print/export Download as PDF; ... For example, [5] the first derivative can be calculated by the complex-step derivative formula: [12] [13] ...

  5. Linear multistep method - Wikipedia

    en.wikipedia.org/wiki/Linear_multistep_method

    The process continues with subsequent steps to map out the solution. Single-step methods (such as Euler's method ) refer to only one previous point and its derivative to determine the current value. Methods such as Runge–Kutta take some intermediate steps (for example, a half-step) to obtain a higher order method, but then discard all ...

  6. Newton's method in optimization - Wikipedia

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

    Newton's method uses curvature information (i.e. the second derivative) to take a more direct route. In calculus , Newton's method (also called Newton–Raphson ) is an iterative method for finding the roots of a differentiable function f {\displaystyle f} , which are solutions to the equation f ( x ) = 0 {\displaystyle f(x)=0} .

  7. Automatic differentiation - Wikipedia

    en.wikipedia.org/wiki/Automatic_differentiation

    [3] [4] Presently, the two types are highly correlated and complementary and both have a wide variety of applications in, e.g., non-linear optimization, sensitivity analysis, robotics, machine learning, computer graphics, and computer vision. [5] [10] [3] [4] [11] [12] Automatic differentiation is particularly important in the field of machine ...

  8. Leapfrog integration - Wikipedia

    en.wikipedia.org/wiki/Leapfrog_integration

    where is position at step , + / is the velocity, or first derivative of , at step + /, = is the acceleration, or second derivative of , at step , and is the size of each time step. These equations can be expressed in a form that gives velocity at integer steps as well: [ 2 ]

  9. Symbolab - Wikipedia

    en.wikipedia.org/wiki/Symbolab

    Symbolab is an answer engine [1] that provides step-by-step solutions to mathematical problems in a range of subjects. [2] It was originally developed by Israeli start-up company EqsQuest Ltd., under whom it was released for public use in 2011. In 2020, the company was acquired by American educational technology website Course Hero. [3] [4]