Contraction mapping (Maryam Ishfaq's question at Yahoo Answers)

In summary, a contraction map on a metric space is a Lipschitz mapping with a constant less than 1, making it uniformly continuous and therefore continuous. This result can be generalized to show that all Lipschitz mappings are uniformly continuous, and thus continuous. Therefore, a contraction map is always a continuous mapping.
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Hello Maryam Ishfaq,

The result is more general. Suppose $(E,d)$ is a metric space and $T:E\to E$ is a Lipschitz mapping, i.e. there is a positive constant $K$ such that $d(T(x),T(y))\le Kd(x,y)$ for all $x,y\in E$. Then, for all $\epsilon >0$ and choosing $\delta=\epsilon/K:$ $$d(x,y)<\delta \Rightarrow d(T(x),T(y))\le Kd(x,y)<K\frac{\epsilon}{K}\Rightarrow d(T(x),T(y))<\epsilon$$ This means that every Lipschitz mapping is uniformly continuous, as a consequence continuous. But a contraction map is a Lipschitz mapping with $K<1$, hence every contraction is a continuous mapping.
 

Related to Contraction mapping (Maryam Ishfaq's question at Yahoo Answers)

1. What is a contraction mapping?

A contraction mapping, also known as a contraction operator, is a mathematical function that maps a metric space to itself and has the property that the distance between the images of any two points is always less than the distance between the original points. In other words, a contraction mapping "contracts" the space by reducing distances between points, and is commonly used in the study of dynamical systems and functional analysis.

2. How is a contraction mapping useful in mathematics?

Contraction mappings are useful in mathematics for various reasons. They are commonly used to prove the existence and uniqueness of solutions to differential equations, and in the study of fixed points and stability of systems. They also have applications in optimization, numerical analysis, and other areas of mathematics and engineering.

3. What is the importance of a contraction mapping in computer science?

In computer science, contraction mappings have several important applications. They are used in algorithms for solving optimization problems, such as gradient descent methods. They are also used in image and signal processing, as well as in data compression and machine learning algorithms.

4. Can you give an example of a contraction mapping?

One example of a contraction mapping is the function f(x)=0.5x, which maps the interval [0,1] to itself. This function satisfies the condition that the distance between the images of any two points in the interval is always less than the distance between the original points. In other words, the points on the interval are "contracted" towards the fixed point of the function, x=0, and the distance between any two points will decrease with each iteration.

5. Are there any limitations or drawbacks to using contraction mappings?

While contraction mappings have many useful applications, they also have some limitations. One limitation is that they may not always converge to a unique fixed point, or may converge to a fixed point that is not the desired solution. Additionally, the convergence rate of a contraction mapping may be slow, requiring many iterations to reach a desired level of accuracy. Finally, the use of contraction mappings may be limited to certain types of problems, and may not be applicable to all mathematical or computational problems.

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