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  2. Monte Carlo methods for option pricing - Wikipedia

    en.wikipedia.org/wiki/Monte_Carlo_methods_for...

    It is based on the iteration of a two step procedure: First, a backward induction process is performed in which a value is recursively assigned to every state at every timestep. The value is defined as the least squares regression against market price of the option value at that state and time (-step). Option value for this regression is ...

  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. 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 ...

  5. 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} .

  6. Backward differentiation formula - Wikipedia

    en.wikipedia.org/wiki/Backward_differentiation...

    The backward differentiation formula (BDF) is a family of implicit methods for the numerical integration of ordinary differential equations.They are linear multistep methods that, for a given function and time, approximate the derivative of that function using information from already computed time points, thereby increasing the accuracy of the approximation.

  7. Finite difference coefficient - Wikipedia

    en.wikipedia.org/wiki/Finite_difference_coefficient

    The theory of Lagrange polynomials provides explicit formulas for the finite difference coefficients. [4] For the first six derivatives we have the following:

  8. Pattern search (optimization) - Wikipedia

    en.wikipedia.org/wiki/Pattern_search_(optimization)

    Pattern search (also known as direct search, derivative-free search, or black-box search) is a family of numerical optimization methods that does not require a gradient. As a result, it can be used on functions that are not continuous or differentiable. One such pattern search method is "convergence" (see below), which is based on the theory of ...

  9. 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.