Difference between revisions of "Iterative method"

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== See also ==
 
== See also ==
  
* [[nth root algorithm]]
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* [[Gradient descent]]
* [[Broyden's method]]
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* [[Heuristic]]
* [[Cryptographically secure pseudorandom number generator]] — a class of functions designed specifically to be unsolvable by root-finding algorithms.
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* [[Matrix splitting]]
* [[GNU Scientific Library]]
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* [[Graeffe's method]]
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* [[MPSolve]]
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* [[Multiplicity (mathematics)]]
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* [[Polynomial greatest common divisor]]
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* [[Polynomial]]
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* [[Root-finding algorithm]]
 
* [[Root-finding algorithm]]
* [[System of polynomial equations]] — root-finding algorithms in the multivariate case
 
  
 
== External links ==
 
== External links ==
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* [https://en.wikipedia.org/wiki/Iterative_method Iterative method] @ Wikipedia
 
* [https://en.wikipedia.org/wiki/Iterative_method Iterative method] @ Wikipedia
  
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[[Category:Computer science]]
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[[Category:Computing]]
 
[[Category:Mathematics]]
 
[[Category:Mathematics]]

Latest revision as of 05:42, 13 October 2016

In computational mathematics, an iterative method is a mathematical procedure that generates a sequence of improving approximate solutions for a class of problems.

Description

A specific implementation of an iterative method, including the termination criteria, is an algorithm of the iterative method.

An iterative method is called convergent if the corresponding sequence converges for given initial approximations.

A mathematically rigorous convergence analysis of an iterative method is usually performed; however, heuristic-based iterative methods are also common.

In the problems of finding the root of an equation (or a solution of a system of equations), an iterative method uses an initial guess to generate successive approximations to a solution.

In contrast, direct methods attempt to solve the problem by a finite sequence of operations. In the absence of rounding errors, direct methods would deliver an exact solution.

Iterative methods are often the only choice for nonlinear equations.

However, iterative methods are often useful even for linear problems involving a large number of variables (sometimes of the order of millions), where direct methods would be prohibitively expensive (and in some cases impossible) even with the best available computing power.

See also

External links