Proving Contraction Mapping: The Linear Map $T$

In summary: On the other hand, $d(x,y)=|x_1-y_1|+|x_2-y_2|$. Since $T$ multiplies the distances by a factor of $\frac{9}{10}$, we can see that $d(T(x),T(y))<d(x,y)$ for
  • #1
ozkan12
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The linear map

$T:{R}^{2}\to {R}^{2}$, $T(x,y)=\left(\frac{8x+8y}{10},\frac{x+y}{10}\right)$ is not a contraction with respect to the Euclidean metric, but is a contraction with respect to

$d(x,y)=\sum_{i=1}^{n}\left| {x}_{i}-{y}_{i} \right| $, $x,y\in {R}^{n}$

contraction constant= 9/10

My questions:

Please show that T is not contraction with respect to Euclidian Metric.

Please show that T is contraction with respect to $d(x,y)=\sum_{i=1}^{n}\left| {x}_{i}-{y}_{i} \right |$ $x,y\in {R}^{n}$
 
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  • #2


Hello,

Thank you for sharing this interesting problem. I will be happy to show why $T$ is not a contraction with respect to the Euclidean metric and why it is a contraction with respect to the metric $d(x,y)$.

First, let's define the Euclidean metric as $d_E(x,y)=\sqrt{(x_1-y_1)^2+(x_2-y_2)^2}$.

To show that $T$ is not a contraction with respect to $d_E$, we need to find two points $x,y\in{R}^2$ such that $d_E(T(x),T(y))>d_E(x,y)$. Let's choose $x=(1,0)$ and $y=(0,1)$. Then, we have $T(x)=\left(\frac{8}{10},\frac{1}{10}\right)$ and $T(y)=\left(\frac{8}{10},\frac{1}{10}\right)$. Therefore, $d_E(T(x),T(y))=\sqrt{(\frac{8}{10}-\frac{1}{10})^2+(\frac{1}{10}-\frac{1}{10})^2}=\sqrt{\frac{49}{100}}=\frac{7}{10}$. However, $d_E(x,y)=\sqrt{(1-0)^2+(0-1)^2}=\sqrt{2}$. Since $\frac{7}{10}>\sqrt{2}$, we can conclude that $T$ is not a contraction with respect to $d_E$.

Next, let's show that $T$ is a contraction with respect to $d(x,y)$. We can rewrite the metric $d(x,y)$ as $d(x,y)=|x_1-y_1|+|x_2-y_2|$. Now, for any two points $x,y\in{R}^2$, we have $d(T(x),T(y))=|T(x)_1-T(y)_1|+|T(x)_2-T(y)_2|=\left|\frac{8x_1+8x_2}{10}-\frac{8y_1+8y_2}{10}\right|+\left|\frac{x_1+x_2}{10}-\frac{y_1+y_2}{10}\right|=\
 

FAQ: Proving Contraction Mapping: The Linear Map $T$

What is a contraction mapping?

A contraction mapping is a type of function that maps a metric space to itself, where the distance between the images of any two points is always less than the distance between the original points. This property is also known as "contractivity", hence the name "contraction mapping".

What is the significance of proving contraction mapping?

Proving contraction mapping is significant because it guarantees the existence and uniqueness of a fixed point, which is a point that is mapped to itself by the function. This is important in many fields of mathematics and science, such as optimization problems and differential equations.

How do you prove that a linear map T is a contraction mapping?

In order to prove that a linear map T is a contraction mapping, we need to show that the norm of the derivative of T is less than 1. This can be done by calculating the operator norm of T and showing that it is less than 1.

What is the Banach Fixed Point Theorem and how does it relate to proving contraction mapping?

The Banach Fixed Point Theorem states that any contraction mapping on a complete metric space has a unique fixed point. This theorem is often used in conjunction with proving contraction mapping, as it provides a powerful tool for showing the existence and uniqueness of a fixed point.

Are there any practical applications of proving contraction mapping?

Yes, proving contraction mapping has many practical applications in fields such as engineering, physics, and economics. For example, it is used in optimization algorithms to find the minimum or maximum of a function, and in solving differential equations to find a unique solution. It is also used in computer science for data compression and in image processing techniques.

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