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==Mathematical definition==
==Mathematical definition==
Let <math>F(x,y)=0</math> be a [[well-posed problem (numerical analysis)|well-posed problem]], i.e. <math>F:X \times Y \rightarrow \mathbb{R}</math> is a [[Real number|real]] or [[Complex Numbers|complex]] functional relationship, defined on the cross-product of an input data set <math>X</math> and an output data set <math>Y</math>, such that exists a [[Lipschitz continuity|locally lipschitz]] function <math>g:X \rightarrow Y</math> called [[Resolvent (direct problem)|resolvent]], which has the property that for every root <math>(x,y)</math> of <math>F</math>, <math>y=g(x)</math>. We define '''numerical method''' for the approximation of <math>F(x,y)=0</math>, the [[sequence]] of problems
Let <math>F(x,y)=0</math> be a [[well-posed problem (numerical analysis)|well-posed problem]], i.e. <math>F:X \times Y \rightarrow \mathbb{R}</math> is a [[Real number|real]] or [[Complex Numbers|complex]] functional relationship, defined on the [[Cartesian product]] of an input data set <math>X</math> and an output data set <math>Y</math>, such that exists a [[Lipschitz continuity|locally lipschitz]] function <math>g:X \rightarrow Y</math> called [[Resolvent (direct problem)|resolvent]], which has the property that for every root <math>(x,y)</math> of <math>F</math>, <math>y=g(x)</math>. We define '''numerical method''' for the approximation of <math>F(x,y)=0</math>, the [[sequence]] of problems


: <math>\left \{ M_n \right \}_{n \in \mathbb{N}} = \left \{ F_n(x_n,y_n)=0 \right \}_{n \in \mathbb{N}},</math>
: <math>\left \{ M_n \right \}_{n \in \mathbb{N}} = \left \{ F_n(x_n,y_n)=0 \right \}_{n \in \mathbb{N}},</math>

Latest revision as of 17:06, 14 April 2025

In numerical analysis, a numerical method is a mathematical tool designed to solve numerical problems. The implementation of a numerical method with an appropriate convergence check in a programming language is called a numerical algorithm.

Mathematical definition

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Let be a well-posed problem, i.e. is a real or complex functional relationship, defined on the Cartesian product of an input data set and an output data set , such that exists a locally lipschitz function called resolvent, which has the property that for every root of , . We define numerical method for the approximation of , the sequence of problems

with , and for every . The problems of which the method consists need not be well-posed. If they are, the method is said to be stable or well-posed.[1]

Consistency

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Necessary conditions for a numerical method to effectively approximate are that and that behaves like when . So, a numerical method is called consistent if and only if the sequence of functions pointwise converges to on the set of its solutions:

When on the method is said to be strictly consistent.[1]

Convergence

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Denote by a sequence of admissible perturbations of for some numerical method (i.e. ) and with the value such that . A condition which the method has to satisfy to be a meaningful tool for solving the problem is convergence:

One can easily prove that the point-wise convergence of to implies the convergence of the associated method.[1]

See also

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References

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  1. ^ a b c Quarteroni, Sacco, Saleri (2000). Numerical Mathematics (PDF). Milano: Springer. p. 33. Archived from the original (PDF) on 2017-11-14. Retrieved 2016-09-27.{{cite book}}: CS1 maint: multiple names: authors list (link)