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  2. Rate of convergence - Wikipedia

    en.wikipedia.org/wiki/Rate_of_convergence

    The definitions of Q-convergence rates have the shortcoming that they do not naturally capture the convergence behavior of sequences that do converge, but do not converge with an asymptotically constant rate with every step, so that the Q-convergence limit does not exist.

  3. Secant method - Wikipedia

    en.wikipedia.org/wiki/Secant_method

    If we compare Newton's method with the secant method, we see that Newton's method converges faster (order 2 against order the golden ratio φ ≈ 1.6). [2] However, Newton's method requires the evaluation of both f {\displaystyle f} and its derivative f ′ {\displaystyle f'} at every step, while the secant method only requires the evaluation ...

  4. Steffensen's method - Wikipedia

    en.wikipedia.org/wiki/Steffensen's_method

    Since the secant method can carry out twice as many steps in the same time as Steffensen's method, [b] in practical use the secant method actually converges faster than Steffensen's method, when both algorithms succeed: the secant method achieves a factor of about (1.6) 22.6 times as many digits for every two steps (two function ...

  5. Regula falsi - Wikipedia

    en.wikipedia.org/wiki/Regula_falsi

    The factor ⁠ 1 / 2 ⁠ used above looks arbitrary, but it guarantees superlinear convergence (asymptotically, the algorithm will perform two regular steps after any modified step, and has order of convergence 1.442). There are other ways to pick the rescaling which give even better superlinear convergence rates. [11]

  6. 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.

  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. Quasi-Newton method - Wikipedia

    en.wikipedia.org/wiki/Quasi-Newton_method

    Quasi-Newton methods are a generalization of the secant method to find the root of the first derivative for multidimensional problems. In multiple dimensions the secant equation is under-determined, and quasi-Newton methods differ in how they constrain the solution, typically by adding a simple low-rank update to the current estimate of the ...

  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: