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Recurrent sequences +:= (), called fixed point iterations, define discrete time autonomous dynamical systems and have important general applications in mathematics through various fixed-point theorems about their convergence behavior.
Convergence in distribution is the weakest form of convergence typically discussed, since it is implied by all other types of convergence mentioned in this article. However, convergence in distribution is very frequently used in practice; most often it arises from application of the central limit theorem .
The rate of convergence is given by the following Berry–Esseen type ... identically distributed random variables is a mixing random process in discrete time ...
The rate of convergence is linear, except for r = 3, when it is dramatically slow, less than linear (see Bifurcation memory). When the parameter 2 < r < 3, except for the initial values 0 and 1, the fixed point = / is the same as when 1 < r ≤ 2. However, in this case the convergence is not monotonically.
For discrete probability ... The rate of return expected by such an ... a.s. is a sufficient condition for convergence of the series by the following ...
The fixed point iteration x n+1 = cos x n with initial value x 1 = −1.. An attracting fixed point of a function f is a fixed point x fix of f with a neighborhood U of "close enough" points around x fix such that for any value of x in U, the fixed-point iteration sequence , (), (()), ((())), … is contained in U and converges to x fix.
The rate of convergence is distinguished from the number of iterations required to reach a given accuracy. For example, the function f ( x ) = x 20 − 1 has a root at 1. Since f ′(1) ≠ 0 and f is smooth, it is known that any Newton iteration convergent to 1 will converge quadratically.
For a set of random variables X n and corresponding set of constants a n (both indexed by n, which need not be discrete), the notation = means that the set of values X n /a n converges to zero in probability as n approaches an appropriate limit. Equivalently, X n = o p (a n) can be written as X n /a n = o p (1), i.e.