Contraction mapping
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 [math]\displaystyle{ 0 \leq k \lt 1 }[/math] such that for all x and y in M,
- [math]\displaystyle{ 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 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 [math]\displaystyle{ f:M \rightarrow N }[/math] is a contractive mapping if there is a constant [math]\displaystyle{ 0 \leq k \lt 1 }[/math] such that
- [math]\displaystyle{ d'(f(x),f(y)) \leq k\,d(x,y) }[/math]
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 [math]\displaystyle{ k=1 }[/math] can be generalized to a firmly non-expansive mapping in a Hilbert space [math]\displaystyle{ \mathcal{H} }[/math] if the following holds for all x and y in [math]\displaystyle{ \mathcal{H} }[/math]:
- [math]\displaystyle{ \|f(x)-f(y) \|^2 \leq \, \langle x-y, f(x) - f(y) \rangle. }[/math]
where
- [math]\displaystyle{ d(x,y) = \|x-y\| }[/math].
This is a special case of [math]\displaystyle{ \alpha }[/math] averaged nonexpansive operators with [math]\displaystyle{ \alpha = 1/2 }[/math].[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 [math]\displaystyle{ \text{Fix}f := \{x \in \mathcal{H} \ | \ f(x) = x\} \neq \varnothing }[/math], then for any initial point [math]\displaystyle{ x_0 \in \mathcal{H} }[/math], iterating
[math]\displaystyle{ (\forall n \in \mathbb{N})\quad x_{n+1} = f(x_n) }[/math]
yields convergence to a fixed point [math]\displaystyle{ x_n \to z \in \text{Fix} f }[/math]. 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
- [math]\displaystyle{ d(f(x), f(y)) \leq d(x,y); }[/math]
- [math]\displaystyle{ d(f(f(x)),f(x)) \lt d(f(x),x) \quad \text{unless} \quad x = f(x). }[/math]
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(x − y). 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
- Short map
- Contraction (operator theory)
- Transformation
References
- ↑ Shifrin, Theodore (2005). Multivariable Mathematics. Wiley. pp. 244–260. ISBN 978-0-471-52638-4.
- ↑ Denardo, Eric V. (1967). "Contraction Mappings in the Theory Underlying Dynamic Programming". SIAM Review 9 (2): 165–177. doi:10.1137/1009030. Bibcode: 1967SIAMR...9..165D.
- ↑ Stokey, Nancy L.; Lucas, Robert E. (1989). Recursive Methods in Economic Dynamics. Cambridge: Harvard University Press. pp. 49–55. ISBN 978-0-674-75096-8. https://books.google.com/books?id=BgQ3AwAAQBAJ&pg=PA49.
- ↑ Combettes, Patrick L. (2004). "Solving monotone inclusions via compositions of nonexpansive averaged operators". Optimization 53 (5–6): 475–504. doi:10.1080/02331930412331327157.
- ↑ 5.0 5.1 Bauschke, Heinz H. (2017). Convex Analysis and Monotone Operator Theory in Hilbert Spaces. New York: Springer.
- ↑ Combettes, Patrick L. (July 2018). "Monotone operator theory in convex optimization". Mathematical Programming B170: 177–206. doi:10.1007/s10107-018-1303-3. Bibcode: 2018arXiv180202694C.
- ↑ Goldstein, A.A. (1967). Constructive real analysis. Harper's Series in Modern Mathematics. New York-Evanston-London: Harper and Row. p. 17.
- ↑ Cain, G. L. Jr.; Nashed, M. Z. (1971). "Fixed Points and Stability for a Sum of Two Operators in Locally Convex Spaces". Pacific Journal of Mathematics 39 (3): 581–592. doi:10.2140/pjm.1971.39.581.
Further reading
- Istratescu, Vasile I. (1981). Fixed Point Theory: An Introduction. Holland: D.Reidel. ISBN 978-90-277-1224-0. provides an undergraduate level introduction.
- Granas, Andrzej; Dugundji, James (2003). Fixed Point Theory. New York: Springer-Verlag. ISBN 978-0-387-00173-9.
- Kirk, William A.; Sims, Brailey (2001). Handbook of Metric Fixed Point Theory. London: Kluwer Academic. ISBN 978-0-7923-7073-4.
- Naylor, Arch W.; Sell, George R. (1982). Linear Operator Theory in Engineering and Science. Applied Mathematical Sciences. 40 (Second ed.). New York: Springer. pp. 125–134. ISBN 978-0-387-90748-2. https://books.google.com/books?id=t3SXs4-KrE0C&pg=PA125.
- Bullo, Francesco (2022). Contraction Theory for Dynamical Systems. Kindle Direct Publishing. ISBN 979-8-8366-4680-6.
Original source: https://en.wikipedia.org/wiki/Contraction mapping.
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