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| In [[numerical analysis]], the speed at which a [[limit of a sequence|convergent sequence]] approaches its limit is called the '''rate of convergence'''. Although strictly speaking, a limit does not give information about any finite first part of the sequence, this concept is of practical importance if we deal with a sequence of successive approximations for an [[iterative method]], as then typically fewer iterations are needed to yield a useful approximation if the rate of convergence is higher. This may even make the difference between needing ten or a million iterations.
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| 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.
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| [[Series acceleration]] is a collection of techniques for improving the rate of convergence of a series discretization . Such acceleration is commonly accomplished with [[sequence transformation]]s.
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| ==Convergence speed for iterative methods==
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| ===Basic definition===
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| Suppose that the sequence {''x''<sub>''k''</sub>} converges to the number L.
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| <span id="linear_convergence">
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| We say that this sequence '''converges linearly''' to L, if there exists a number μ ∈ (0, 1) such that
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| :<math> \lim_{k\to \infty} \frac{|x_{k+1}-L|}{|x_k-L|} = \mu.</math>
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| The number μ is called the ''rate of convergence''. | |
| </span>
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| If the sequence converges, and
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| * μ = 0, then the sequence is said to '''converge superlinearly'''.
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| * μ = 1, then the sequence is said to '''converge sublinearly'''.
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| If the sequence ''converges sublinearly'' and additionally
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| :<math> \lim_{k\to \infty} \frac{|x_{k+2} - x_{k+1}|}{|x_{k+1} - x_k|} = 1,</math>
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| then it is said the sequence {''x''<sub>''k''</sub>} '''converges logarithmically''' to L.
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| The next definition is used to distinguish superlinear rates of convergence. We say that the sequence '''converges with order ''q'' to L''' for ''q''>1<ref>''q'' may be non-integer. For example, the [[secant method]] has, in the case of convergence to a regular root, convergence order [[golden ratio|φ]]=1.618....</ref> if
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| :<math> \lim_{k\to \infty} \frac{|x_{k+1}-L|}{|x_k-L|^q} = \mu \,\big|\; \mu > 0.</math>
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| In particular, convergence with order
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| * ''q'' = 2 is called '''quadratic convergence''',
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| * ''q'' = 3 is called '''cubic convergence''',
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| * etc.
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| This is sometimes called ''Q-linear convergence'', ''Q-quadratic convergence'', etc., to distinguish it from the definition below. The Q stands for "quotient," because the definition uses the quotient between two successive terms.
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| ===Extended definition===
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| The drawback of the above definitions is that these do not catch some sequences which still converge reasonably fast, but whose "speed" is variable, such as the sequence {''b''<sub>''k''</sub>} below. Therefore, the definition of rate of convergence is sometimes extended as follows.
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| Under the new definition, the sequence {''x''<sub>''k''</sub>} converges with at least order ''q'' if there exists a sequence {ε<sub>''k''</sub>} such that
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| :<math>|x_k - L|\le\varepsilon_k\quad\mbox{for all }k,</math>
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| and the sequence {ε<sub>''k''</sub>} converges to zero with order ''q'' according to the above "simple" definition. To distinguish it from that definition, this is sometimes called ''R-linear convergence'', ''R-quadratic convergence'', etc. (with the R standing for "root"). | |
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| ===Examples===
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| Consider the following sequences:
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| :<math>\begin{align}
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| a_0 &= 1 ,\, &&a_1 = \frac12 ,\, &&a_2 = \frac14 ,\, &&a_3 = \frac18 ,\, &&a_4 = \frac1{16} ,\, &&a_5 = \frac1{32} ,\, &&\ldots ,\, &&a_k = \frac1{2^k} ,\, &&\ldots \\
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| b_0 &= 1 ,\, &&b_1 = 1 ,\, &&b_2 = \frac14 ,\, &&b_3 = \frac14 ,\, &&b_4 = \frac1{16} ,\, &&b_5 = \frac1{16} ,\, &&\ldots ,\, &&b_k = \frac1{4^{\left\lfloor \frac{k}{2} \right\rfloor}} ,\, &&\ldots \\
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| c_0 &= \frac12 ,\, &&c_1 = \frac14 ,\, &&c_2 = \frac1{16} ,\, &&c_3 = \frac1{256} ,\, &&c_4 = \frac1{65\,536} ,\, &&&&\ldots ,\, &&c_k = \frac1{2^{2^k}} ,\, &&\ldots \\
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| d_0 &= 1 ,\, &&d_1 = \frac12 ,\, &&d_2 = \frac13 ,\, &&d_3 = \frac14 ,\, &&d_4 = \frac15 ,\, &&d_5 = \frac16 ,\, &&\ldots ,\, &&d_k = \frac1{k+1} ,\, &&\ldots
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| \end{align}</math>
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| The sequence {''a''<sub>''k''</sub>} converges linearly to 0 with rate 1/2. More generally, the sequence ''Cμ''<sup>''k''</sup> converges linearly with rate μ if |μ| < 1. The sequence {''b''<sub>''k''</sub>} also converges linearly to 0 with rate 1/2 under the extended definition, but not under the simple definition. The sequence {''c''<sub>''k''</sub>} converges superlinearly. In fact, it is quadratically convergent. Finally, the sequence {''d''<sub>''k''</sub>} converges sublinearly.
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| [[Image:ConvergencePlots.png|thumb|alt=Plot showing the different rates of convergence for the sequences a_k, b_k, c_k and d_k.|Linear, Linear, Superlinear/Quadratic and sublinear rate of convergence.|400px|center]]
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| ==Convergence speed for discretization methods==
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| A similar situation exists for discretization methods. The important parameter here for the convergence speed is not the iteration number ''k'' but it depends on the number of grid points and grid spacing. In this case, the number of grid points in a discretization process is inversely proportional to the grid spacing here denoted as ''n''.
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| In this case, a sequence <math>x_n</math> is said to converge to L with order ''p'' if there exists a constant ''C'' such that
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| :<math> |x_n - L| < Cn^{-p} \text{ for all } n. </math>
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| This is written as |''x''<sub>''n''</sub> - L| = O(''n''<sup>-''p''</sup>) using the [[big O notation]].
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| This is the relevant definition when discussing methods for [[numerical quadrature]] or the [[numerical ordinary differential equations|solution of ordinary differential equations]].
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| === Examples ===
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| The sequence {''d''<sub>''k''</sub>} with ''d''<sub>''k''</sub> = 1 / (''k''+1) was introduced above. This sequence converges with order 1 according to the convention for discretization methods.
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| The sequence {''a''<sub>''k''</sub>} with ''a''<sub>''k''</sub> = 2<sup>-''k''</sup>, which was also introduced above, converges with order ''p'' for every number ''p''. 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.
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| The order of convergence of a discretization method is related to its [[Truncation error (numerical integration)|Global Truncation Error (GTE)]].
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| ==Acceleration of convergence==
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| Many methods exist to increase the rate of convergence of a given sequence,
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| i.e. to [[sequence transformation|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 be much less "expensive" to calculate than the original sequence. One example of series acceleration is [[Aitken's delta-squared process]].
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| ==References==
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| <references />
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| ==Literature==
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| The simple definition is used in
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| * Michelle Schatzman (2002), ''Numerical analysis: a mathematical introduction'', Clarendon Press, Oxford. ISBN 0-19-850279-6.
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| The extended definition is used in
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| * Kendell A. Atkinson (1988), ''An introduction to numerical analysis'' (2nd ed.), John Wiley and Sons. ISBN 0-471-50023-2.
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| * http://web.mit.edu/rudin/www/MukherjeeRuSc11COLT.pdf
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| * Walter Gautschi (1997), ''Numerical analysis: an introduction,'' Birkhäuser, Boston. ISBN 0-8176-3895-4.
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| * [[Endre Süli]] and David Mayers (2003), ''An introduction to numerical analysis,'' Cambridge University Press. ISBN 0-521-00794-1.
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| Logarithmic convergence is used in
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| * {{cite journal | last = Van Tuyl | first = Andrew H. | year = 1994 | title = Acceleration of convergence of a family of logarithmically convergent sequences | journal = Mathematics of Computation | volume = 63 | issue = 207 | pages = 229–246 | url = http://www.ams.org/journals/mcom/1994-63-207/S0025-5718-1994-1234428-2/S0025-5718-1994-1234428-2.pdf}}
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| The Big O definition is used in
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| *Richard L. Burden and J. Douglas Faires (2001), ''Numerical Analysis'' (7th ed.), Brooks/Cole. ISBN 0-534-38216-9
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| The terms ''Q-linear'' and ''R-linear'' are used in; The Big O definition when using Taylor series is used in
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| * {{Cite book | last1=Nocedal | first1=Jorge | last2=Wright | first2=Stephen J. | title=Numerical Optimization | publisher=[[Springer-Verlag]] | location=Berlin, New York | edition=2nd | isbn=978-0-387-30303-1 | year=2006 | pages=619+620 | postscript=<!-- Bot inserted parameter. Either remove it; or change its value to "." for the cite to end in a ".", as necessary. -->{{inconsistent citations}}}}.
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| One may also study the following paper for Q-linear and R-linear:
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| * {{cite journal | last = Potra | first = F. A. | year = 1989 | title = On Q-order and R-order of convergence | journal = J. Optim. Th. Appl. | volume = 63 | issue = 3 | pages = 415–431 | doi = 10.1007/BF00939805}}
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| [[Category:Numerical analysis]]
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