Contraction mapping: Difference between revisions

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{{Short description|Function reducing distance between all points}} In [[mathematics]], a '''contraction mapping''', or '''contraction''' or '''contractor''', on a [[metric space]] (''M'',&thinsp;''d'') is a [[Function (mathematics)|function]] ''f'' from ''M'' to itself, with the property that there is some [[real number]] <math>0 \leq k < 1</math> such that for all ''x'' and ''y'' in ''M'',
{{Short description|Function reducing distance between all points}} In [[mathematics]], a '''contraction mapping''', or '''contraction''' or '''contractor''', on a [[metric space]] (''M'',&thinsp;''d'') is a [[Function (mathematics)|function]] ''f'' from ''M'' to itself, with the property that there is some [[real number]] <math>0 \leq k < 1</math> such that for all ''x'' and ''y'' in ''M'',
 
<math display="block">d(f(x),f(y)) \leq k\,d(x,y).</math>
:<math>d(f(x),f(y)) \leq k\,d(x,y).</math>
The smallest such value of ''k'' is called the '''[[Lipschitz constant]]''' of ''f''.  Contractive maps are sometimes called '''Lipschitzian maps'''.  If the above condition is instead satisfied for
The smallest such value of ''k'' is called the '''Lipschitz constant''' of ''f''.  Contractive maps are sometimes called '''Lipschitzian maps'''.  If the above condition is instead satisfied for
''k''&nbsp;≤&nbsp;1, then the mapping is said to be a [[non-expansive map]].
''k''&nbsp;≤&nbsp;1, then the mapping is said to be a [[non-expansive map]].


More generally, the idea of a contractive mapping can be defined for maps between metric spaces. Thus, if (''M'',&thinsp;''d'') and (''N'',&thinsp;''d''') are two metric spaces, then <math>f:M \rightarrow N</math> is a contractive mapping if there is a constant <math>0 \leq k < 1</math> such that
More generally, the idea of a contractive mapping can be defined for maps between metric spaces. Thus, if (''M'',&thinsp;''d'') and (''N'',&thinsp;''d''') are two metric spaces, then <math>f:M \rightarrow N</math> is a contractive mapping if there is a constant <math>0 \leq k < 1</math> such that
:<math>d'(f(x),f(y)) \leq k\,d(x,y)</math>
<math display="block">d'(f(x),f(y)) \leq k\,d(x,y)</math>
for all ''x'' and ''y'' in ''M''.
for all ''x'' and ''y'' in ''M''.


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==Firmly non-expansive mapping==
==Firmly non-expansive mapping==
A non-expansive mapping with <math>k=1</math> can be generalized to a '''firmly non-expansive mapping''' in a [[Hilbert space]] <math>\mathcal{H}</math> if the following holds for all ''x'' and ''y'' in <math>\mathcal{H}</math>:
A non-expansive mapping with <math>k=1</math> can be generalized to a '''firmly non-expansive mapping''' in a [[Hilbert space]] <math>\mathcal{H}</math> if the following holds for all ''x'' and ''y'' in <math>\mathcal{H}</math>:
:<math>\|f(x)-f(y) \|^2 \leq \, \langle x-y, f(x) - f(y) \rangle.</math>
<math display="block">\|f(x)-f(y) \|^2 \leq \, \langle x-y, f(x) - f(y) \rangle,</math>
where
where
:<math>d(x,y) = \|x-y\|</math>.
<math display="block">d(x,y) = \|x-y\|.</math>
 
This is a special case of <math>\alpha</math> averaged nonexpansive operators with <math>\alpha = 1/2</math>.<ref>{{cite journal |title=Solving monotone inclusions via compositions of nonexpansive averaged operators |first=Patrick L. |last=Combettes |year=2004 |journal=[[Optimization (journal)|Optimization]] |volume=53 |issue=5–6 |pages=475–504 |doi=10.1080/02331930412331327157 |s2cid=219698493 }}</ref> A firmly non-expansive mapping is always non-expansive, via the [[Cauchy–Schwarz inequality]].
This is a special case of <math>\alpha</math> averaged nonexpansive operators with <math>\alpha = 1/2</math>.<ref>{{cite journal |title=Solving monotone inclusions via compositions of nonexpansive averaged operators |first=Patrick L. |last=Combettes |year=2004 |journal=[[Optimization (journal)|Optimization]] |volume=53 |issue=5–6 |pages=475–504 |doi=10.1080/02331930412331327157 |s2cid=219698493 }}</ref> A firmly non-expansive mapping is always non-expansive, via the [[Cauchy–Schwarz inequality]].


The class of firmly non-expansive maps is closed under [[convex combination]]s, but not compositions.<ref name=":0">{{Cite book|title=Convex Analysis and Monotone Operator Theory in Hilbert Spaces|last=Bauschke|first=Heinz H.|publisher=Springer|year=2017|location=New York}}</ref> This class includes [[Proximal operator|proximal mappings]] of proper, convex, lower-semicontinuous functions, hence it also includes orthogonal [[Projection (mathematics)|projections]] onto non-empty closed [[convex set]]s. The class of firmly nonexpansive operators is equal to the set of resolvents of maximally [[Monotonic function#Monotonicity in functional analysis|monotone operators]].<ref>{{Cite journal|last=Combettes|first=Patrick L.|date=July 2018|title=Monotone operator theory in convex optimization|journal=Mathematical Programming|volume=B170|pages=177–206|arxiv=1802.02694|doi=10.1007/s10107-018-1303-3|bibcode=2018arXiv180202694C|s2cid=49409638}}</ref> Surprisingly, while iterating non-expansive maps has no guarantee to find a fixed point (e.g. multiplication by -1), firm non-expansiveness is sufficient to [[convergence proof techniques|guarantee global convergence]] to a fixed point, provided a fixed point exists. More precisely, if <math>\operatorname{Fix}f := \{x \in \mathcal{H} \ | \ f(x) = x\} \neq \varnothing</math>, then for any initial point <math>x_0 \in \mathcal{H}</math>, iterating
The class of firmly non-expansive maps is closed under [[convex combination]]s, but not compositions.<ref name=":0">{{Cite book|title=Convex Analysis and Monotone Operator Theory in Hilbert Spaces|last=Bauschke|first=Heinz H.|publisher=Springer|year=2017|location=New York}}</ref> This class includes [[Proximal operator|proximal mappings]] of proper, convex, lower-semicontinuous functions, hence it also includes orthogonal [[Projection (mathematics)|projections]] onto non-empty closed [[convex set]]s. The class of firmly nonexpansive operators is equal to the set of resolvents of maximally [[Monotonic function#Monotonicity in functional analysis|monotone operators]].<ref>{{Cite journal|last=Combettes|first=Patrick L.|date=July 2018|title=Monotone operator theory in convex optimization|journal=Mathematical Programming|volume=B170|pages=177–206|arxiv=1802.02694|doi=10.1007/s10107-018-1303-3|bibcode=2018arXiv180202694C|s2cid=49409638}}</ref> Surprisingly, while iterating non-expansive maps has no guarantee to find a fixed point (e.g. multiplication by -1), firm non-expansiveness is sufficient to [[convergence proof techniques|guarantee global convergence]] to a fixed point, provided a fixed point exists. More precisely, if
 
<math display="block">\operatorname{Fix}f := \{x \in \mathcal{H} \ | \ f(x) = x\} \neq \varnothing,</math>
<math> (\forall n \in \mathbb{N})\quad x_{n+1} = f(x_n) </math>
then for any initial point <math>x_0 \in \mathcal{H}</math>, iterating
 
<math display="block"> x_{n+1} = f(x_n), \quad \forall n \in \mathbb{N}</math>
yields convergence to a fixed point <math> x_n \to z \in \operatorname{Fix} f</math>. This convergence might be [[Weak convergence (Hilbert space)|weak]] in an infinite-dimensional setting.<ref name=":0" />
yields convergence to a fixed point <math> x_n \to z \in \operatorname{Fix} f</math>. This convergence might be [[Weak convergence (Hilbert space)|weak]] in an infinite-dimensional setting.<ref name=":0" />


==Subcontraction map==
==Subcontraction map==
A '''subcontraction map''' or '''subcontractor''' is a map ''f'' on a metric space (''M'',&thinsp;''d'') such that
A '''subcontraction map''' or '''subcontractor''' is a map ''f'' on a metric space (''M'',&thinsp;''d'') such that
 
<math display="block"> d(f(x), f(y)) \leq d(x,y);</math>
:<math> d(f(x), f(y)) \leq d(x,y);</math>
<math display="block">d(f(f(x)),f(x)) < d(f(x),x) \quad \text{unless} \quad x = f(x).</math>
:<math> d(f(f(x)),f(x)) < d(f(x),x) \quad \text{unless} \quad x = f(x).</math>
 
If the [[Image (mathematics)|image]] of a subcontractor ''f'' is [[Compact space|compact]], then ''f'' has a fixed point.<ref name=Gold17>{{cite book | last=Goldstein | first=A.A. | title=Constructive real analysis | zbl=0189.49703 | series=Harper's Series in Modern Mathematics | location=New York-Evanston-London | publisher=Harper and Row | year=1967 |page=17 }}</ref>
If the [[Image (mathematics)|image]] of a subcontractor ''f'' is [[Compact space|compact]], then ''f'' has a fixed point.<ref name=Gold17>{{cite book | last=Goldstein | first=A.A. | title=Constructive real analysis | zbl=0189.49703 | series=Harper's Series in Modern Mathematics | location=New York-Evanston-London | publisher=Harper and Row | year=1967 |page=17 }}</ref>



Latest revision as of 07:42, 21 July 2025

Template:Short description In mathematics, a contraction mapping, or contraction or contractor, on a metric space (M, d) is a function f from M to itself, with the property that there is some real number 0k<1 such that for all x and y in M, d(f(x),f(y))kd(x,y). The smallest such value of k is called the Lipschitz constant of f. Contractive maps are sometimes called Lipschitzian maps. If the above condition is instead satisfied for k ≤ 1, then the mapping is said to be a non-expansive map.

More generally, the idea of a contractive mapping can be defined for maps between metric spaces. Thus, if (M, d) and (N, d') are two metric spaces, then f:MN is a contractive mapping if there is a constant 0k<1 such that d(f(x),f(y))kd(x,y) for all x and y in M.

Every contraction mapping is Lipschitz continuous and hence uniformly continuous (for a Lipschitz continuous function, the constant k is no longer necessarily less than 1).

A contraction mapping has at most one fixed point. Moreover, the Banach fixed-point theorem states that every contraction mapping on a non-empty complete metric space has a unique fixed point, and that for any x in M the iterated function sequence x, f (x), f (f (x)), f (f (f (x))), ... converges to the fixed point. This concept is very useful for iterated function systems where contraction mappings are often used. Banach's fixed-point theorem is also applied in proving the existence of solutions of ordinary differential equations, and is used in one proof of the inverse function theorem.[1]

Contraction mappings play an important role in dynamic programming problems.[2][3]

Firmly non-expansive mapping

A non-expansive mapping with k=1 can be generalized to a firmly non-expansive mapping in a Hilbert space if the following holds for all x and y in : f(x)f(y)2xy,f(x)f(y), where d(x,y)=xy. This is a special case of α averaged nonexpansive operators with α=1/2.[4] A firmly non-expansive mapping is always non-expansive, via the Cauchy–Schwarz inequality.

The class of firmly non-expansive maps is closed under convex combinations, but not compositions.[5] This class includes proximal mappings of proper, convex, lower-semicontinuous functions, hence it also includes orthogonal projections onto non-empty closed convex sets. The class of firmly nonexpansive operators is equal to the set of resolvents of maximally monotone operators.[6] Surprisingly, while iterating non-expansive maps has no guarantee to find a fixed point (e.g. multiplication by -1), firm non-expansiveness is sufficient to guarantee global convergence to a fixed point, provided a fixed point exists. More precisely, if Fixf:={x | f(x)=x}, then for any initial point x0, iterating xn+1=f(xn),n yields convergence to a fixed point xnzFixf. This convergence might be weak in an infinite-dimensional setting.[5]

Subcontraction map

A subcontraction map or subcontractor is a map f on a metric space (M, d) such that d(f(x),f(y))d(x,y); d(f(f(x)),f(x))<d(f(x),x)unlessx=f(x). If the image of a subcontractor f is compact, then f has a fixed point.[7]

Locally convex spaces

In a locally convex space (E, P) with topology given by a set P of seminorms, one can define for any p ∈ P a p-contraction as a map f such that there is some kp < 1 such that p(f(x) − f(y))kp p(xy). If f is a p-contraction for all p ∈ P and (E, P) is sequentially complete, then f has a fixed point, given as limit of any sequence xn+1 = f(xn), and if (E, P) is Hausdorff, then the fixed point is unique.[8]

See also

References

Template:Reflist

Further reading

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Template:Metric spaces

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