In mathematical analysis, particularly numerical analysis, the rate of convergence and order of convergence of a sequence that converges to a limit are any of several characterizations of how quickly that sequence approaches its limit. These are broadly divided into rates and orders of convergence that describe how quickly a sequence further approaches its limit once it is already close to it, called asymptotic rates and orders of convergence, and those that describe how quickly sequences approach their limits from starting points that are not necessarily close to their limits, called non-asymptotic rates and orders of convergence. Asymptotic behavior is particularly useful for deciding when to stop a sequence of numerical computations, for instance once a target precision has been reached with an iterative root-finding algorithm, but pre-asymptotic behavior is often crucial for determining whether to begin a sequence of computations at all, since it may be impossible or impractical to ever reach a target precision with a poorly chosen approach. Asymptotic rates and orders of convergence are the focus of this article. In practical numerical computations, asymptotic rates and orders of convergence follow two common conventions for two types of sequences: the first for sequences of iterations of an iterative numerical method and the second for sequences of successively more accurate numerical discretizations of a target. In formal mathematics, rates of convergence and orders of convergence are often described comparatively using asymptotic notation commonly called "big O notation," which can be used to encompass both of the prior conventions; this is an application of asymptotic analysis. For iterative methods, a sequence ( x k ) {\displaystyle (x_{k})} that converges to L {\displaystyle L} is said to have asymptotic order of convergence q ≥ 1 {\displaystyle q\geq 1} and asymptotic rate of convergence μ {\displaystyle \mu } if
lim k → ∞ | x k + 1 − L | | x k − L | q = μ . {\displaystyle \lim _{k\rightarrow \infty }{\frac {\left|x_{k+1}-L\right|}{\left|x_{k}-L\right|^{q}}}=\mu .}
Where methodological precision is required, these rates and orders of convergence are known specifically as the rates and orders of Q-convergence, short for quotient-convergence, since the limit in question is a quotient of error terms. The rate of convergence μ {\displaystyle \mu } may also be called the asymptotic error constant, and some authors will use rate where this article uses order. Series acceleration methods are techniques for improving the rate of convergence of the sequence of partial sums of a series and possibly its order of convergence, also. Similar concepts are used for sequences of discretizations. 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. A sequence of approximate grid solutions ( y k ) {\displaystyle (y_{k})} of some problem that converges to a true solution S {\displaystyle S} with a corresponding sequence of regular grid spacings ( h k ) {\displaystyle (h_{k})} that converge to 0 is said to have asymptotic order of convergence q {\displaystyle q} and asymptotic rate of convergence μ {\displaystyle \mu } if
lim k → ∞ | y k − S | h k q = μ , {\displaystyle \lim _{k\rightarrow \infty }{\frac {\left|y_{k}-S\right|}{h_{k}^{q}}}=\mu ,}
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