In numerical analysis, the order of convergence and the rate of convergence of a convergent sequence are quantities that represent how quickly the sequence approaches its limit. A sequence that converges to is said to have order of convergence and rate of convergence if
The rate of convergence is also called the asymptotic error constant. Note that this terminology is not standardized and some authors will use rate where this article uses order (e.g., ^{[2]}).
In practice, the rate and order of convergence provide useful insights when using iterative methods for calculating numerical approximations. If the order of convergence is higher, then typically fewer iterations are necessary to yield a useful approximation. Strictly speaking, however, the asymptotic behavior of a sequence does not give conclusive information about any finite part of the sequence.
Similar concepts are used for discretization methods. The solution of the discretized problem converges to the solution of the continuous problem as the grid size goes to zero, and the speed of convergence is one of the factors of the efficiency of the method. However, the terminology, in this case, is different from the terminology for iterative methods.
Series acceleration is a collection of techniques for improving the rate of convergence of a series discretization. Such acceleration is commonly accomplished with sequence transformations.
Suppose that the sequence converges to the number . The sequence is said to converge with order to , and with a rate of convergence^{[3]} of , if

(Definition 1) 
for some positive constant if , and if .^{[4]}^{[5]} It is not necessary, however, that be an integer. For example, the secant method, when converging to a regular, simple root, has an order of φ ≈ 1.618.^{[citation needed]}
Convergence with order
A practical method to calculate the order of convergence for a sequence generated by a fixed point iteration is to calculate the following sequence, which converges to :^{[6]}
For numerical approximation of an exact value through a numerical method of order q see ^{[7]}
In addition to the previously defined Qlinear convergence, a few other Qconvergence definitions exist. Given Definition 1 defined above, the sequence is said to converge Qsuperlinearly to (i.e. faster than linearly) in all the cases where and also the case .^{[8]} Given Definition 1, the sequence is said to converge Qsublinearly to (i.e. slower than linearly) if . The sequence converges logarithmically to if the sequence converges sublinearly and additionally if^{[9]}
In the definitions above, the "Q" stands for "quotient" because the terms are defined using the quotient between two successive terms.^{[10]}^{: 619 } Often, however, the "Q" is dropped and a sequence is simply said to have linear convergence, quadratic convergence, etc.
The Qconvergence definitions have a shortcoming in that they do not include some sequences, such as the sequence below, which converge reasonably fast, but whose rate is variable. Therefore, the definition of rate of convergence is extended as follows.
Suppose that converges to . The sequence is said to converge Rlinearly to if there exists a sequence such that
Consider the sequence
The sequence
The sequence
Finally, the sequence
A similar situation exists for discretization methods designed to approximate a function , which might be an integral being approximated by numerical quadrature, or the solution of an ordinary differential equation (see example below). The discretization method generates a sequence , where each successive is a function of along with the grid spacing between successive values of the independent variable . The important parameter here for the convergence speed to is the grid spacing , inversely proportional to the number of grid points, i.e. the number of points in the sequence required to reach a given value of .
In this case, the sequence is said to converge to the sequence with order q if there exists a constant C such that
This is written as using big O notation.
This is the relevant definition when discussing methods for numerical quadrature or the solution of ordinary differential equations (ODEs).^{[example needed]}
A practical method to estimate the order of convergence for a discretization method is pick step sizes and and calculate the resulting errors and . The order of convergence is then approximated by the following formula:
which comes from writing the truncation error, at the old and new grid spacings, as
The error is, more specifically, a global truncation error (GTE), in that it represents a sum of errors accumulated over all iterations, as opposed to a local truncation error (LTE) over just one iteration.
Consider the ordinary differential equation
with initial condition . We can solve this equation using the Forward Euler scheme for numerical discretization:
which generates the sequence
In terms of , this sequence is as follows, from the Binomial theorem:
The exact solution to this ODE is , corresponding to the following Taylor expansion in for :
In this case, the truncation error is
so converges to with a convergence rate .
The sequence with was introduced above. This sequence converges with order 1 according to the convention for discretization methods.^{[why?]}
The sequence with , which was also introduced above, converges with order q for every number q. It is said to converge exponentially using the convention for discretization methods. However, it only converges linearly (that is, with order 1) using the convention for iterative methods.^{[why?]}
The case of recurrent sequences which occurs in dynamical systems and in the context of various fixedpoint theorems is of particular interest. Assuming that the relevant derivatives of f are continuous, one can (easily) show that for a fixed point such that , one has at least linear convergence for any starting value sufficiently close to p. If and , then one has at least quadratic convergence, and so on. If , then one has a repulsive fixed point and no starting value will produce a sequence converging to p (unless one directly jumps to the point p itself).
Many methods exist to increase the rate of convergence of a given sequence, i.e. to transform a given sequence into one converging faster to the same limit. Such techniques are in general known as "series acceleration". The goal of the transformed sequence is to reduce the computational cost of the calculation. One example of series acceleration is Aitken's deltasquared process. These methods in general (and in particular Aitken's method) do not increase the order of convergence, and are useful only if initially the convergence is not faster than linear: If convergences linearly, one gets a sequence that still converges linearly (except for pathologically designed special cases), but faster in the sense that . On the other hand, if the convergence is already of order ≥ 2, Aitken's method will bring no improvement.
The simple definition is used in
The extended definition is used in
The Big O definition is used in
The terms Qlinear and Rlinear are used in