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  2. Rate of convergence - Wikipedia

    en.wikipedia.org/wiki/Rate_of_convergence

    For instance, ideally the solution of a differential equation discretized via a regular grid will converge to the solution of the continuous equation as the grid spacing goes to zero, and if so the asymptotic rate and order of that convergence are important properties of the gridding method.

  3. Regula falsi - Wikipedia

    en.wikipedia.org/wiki/Regula_falsi

    That problem isn't unique to regula falsi: Other than bisection, all of the numerical equation-solving methods can have a slow-convergence or no-convergence problem under some conditions. Sometimes, Newton's method and the secant method diverge instead of converging – and often do so under the same conditions that slow regula falsi's convergence.

  4. Muller's method - Wikipedia

    en.wikipedia.org/wiki/Muller's_method

    Muller's method is a root-finding algorithm, a numerical method for solving equations of the form f(x) = 0.It was first presented by David E. Muller in 1956.. Muller's method proceeds according to a third-order recurrence relation similar to the second-order recurrence relation of the secant method.

  5. Anderson acceleration - Wikipedia

    en.wikipedia.org/wiki/Anderson_acceleration

    Given a function :, consider the problem of finding a fixed point of , which is a solution to the equation () =.A classical approach to the problem is to employ a fixed-point iteration scheme; [2] that is, given an initial guess for the solution, to compute the sequence + = until some convergence criterion is met.

  6. Secant method - Wikipedia

    en.wikipedia.org/wiki/Secant_method

    In numerical analysis, the secant method is a root-finding algorithm that uses a succession of roots of secant lines to better approximate a root of a function f. The secant method can be thought of as a finite-difference approximation of Newton's method , so it is considered a quasi-Newton method .

  7. Aitken's delta-squared process - Wikipedia

    en.wikipedia.org/wiki/Aitken's_delta-squared_process

    In numerical analysis, Aitken's delta-squared process or Aitken extrapolation is a series acceleration method used for accelerating the rate of convergence of a sequence. It is named after Alexander Aitken, who introduced this method in 1926. [1] It is most useful for accelerating the convergence of a sequence that is converging linearly.

  8. Brent's method - Wikipedia

    en.wikipedia.org/wiki/Brent's_method

    Suppose that we want to solve the equation f(x) = 0. As with the bisection method, we need to initialize Dekker's method with two points, say a 0 and b 0, such that f(a 0) and f(b 0) have opposite signs. If f is continuous on [a 0, b 0], the intermediate value theorem guarantees the existence of a solution between a 0 and b 0.

  9. Halley's method - Wikipedia

    en.wikipedia.org/wiki/Halley's_method

    Halley's method is a numerical algorithm for solving the nonlinear equation f(x) = 0.In this case, the function f has to be a function of one real variable. The method consists of a sequence of iterations: