Numerical method

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

Let ${\displaystyle F(x,y)=0}$ be a well-posed problem, i.e. ${\displaystyle F:X\times Y\rightarrow \mathbb {R} }$ is a real or complex functional relationship, defined on the cross-product of an input data set ${\displaystyle X}$ and an output data set ${\displaystyle Y}$, such that exists a locally lipschitz function ${\displaystyle g:X\rightarrow Y}$ called resolvent, which has the property that for every root ${\displaystyle (x,y)}$ of ${\displaystyle F}$, ${\displaystyle y=g(x)}$. We define numerical method for the approximation of ${\displaystyle F(x,y)=0}$, the sequence of problems

${\displaystyle \left\{M_{n}\right\}_{n\in \mathbb {N} }=\left\{F_{n}(x_{n},y_{n})=0\right\}_{n\in \mathbb {N} },}$

with ${\displaystyle F_{n}:X_{n}\times Y_{n}\rightarrow \mathbb {R} }$, ${\displaystyle x_{n}\in X_{n}}$ and ${\displaystyle y_{n}\in Y_{n}}$ for every ${\displaystyle n\in \mathbb {N} }$. 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

Necessary conditions for a numerical method to effectively approximate ${\displaystyle F(x,y)=0}$ are that ${\displaystyle x_{n}\rightarrow x}$ and that ${\displaystyle F_{n}}$ behaves like ${\displaystyle F}$ when ${\displaystyle n\rightarrow \infty }$. So, a numerical method is called consistent if and only if the sequence of functions ${\displaystyle \left\{F_{n}\right\}_{n\in \mathbb {N} }}$ pointwise converges to ${\displaystyle F}$ on the set ${\displaystyle S}$ of its solutions:

${\displaystyle \lim F_{n}(x,y+t)=F(x,y,t)=0,\quad \quad \forall (x,y,t)\in S.}$

When ${\displaystyle F_{n}=F,\forall n\in \mathbb {N} }$ on ${\displaystyle S}$ the method is said to be strictly consistent.^[1]

Convergence

Denote by ${\displaystyle \ell _{n}}$ a sequence of admissible perturbations of ${\displaystyle x\in X}$ for some numerical method ${\displaystyle M}$ (i.e. ${\displaystyle x+\ell _{n}\in X_{n}\forall n\in \mathbb {N} }$) and with ${\displaystyle y_{n}(x+\ell _{n})\in Y_{n}}$ the value such that ${\displaystyle F_{n}(x+\ell _{n},y_{n}(x+\ell _{n}))=0}$. A condition which the method has to satisfy to be a meaningful tool for solving the problem ${\displaystyle F(x,y)=0}$ is convergence:

${\displaystyle {\begin{aligned}&\forall \varepsilon >0,\exists n_{0}(\varepsilon )>0,\exists \delta _{\varepsilon ,n_{0}}{\text{ such that}}\\&\forall n>n_{0},\forall \ell _{n}:\|\ell _{n}\|<\delta _{\varepsilon ,n_{0}}\Rightarrow \|y_{n}(x+\ell _{n})-y\|\leq \varepsilon .\end{aligned}}}$

One can easily prove that the point-wise convergence of ${\displaystyle \{y_{n}\}_{n\in \mathbb {N} }}$ to ${\displaystyle y}$ implies the convergence of the associated method is function.^[1]

See also

• Numerical methods for ordinary differential equations
• Numerical methods for partial differential equations

References