IADPCFEVER's question at Yahoo Answers (projection and linear transformation)

In summary: Therefore, $p$ is a linear transformation from $\mathbb{R}^2$ to $\mathbb{R}^2$. In summary, the transformation $p$ from $\mathbb{R}^2$ to $\mathbb{R}^2$ that projects a vector onto the line $y=x$ can be shown to be linear by representing it as a matrix transformation.
  • #1
Fernando Revilla
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Here is the question:

I'm supposed to show that this transformation from R^2 to R^2 is linear by showing that it is a matrix transformation.

P projects a vector onto the line y=x

How do I go about?

Here is a link to the question:

Projection and linear transformation? - Yahoo! Answers

I have posted a link there to this topic so the OP can find my response.
 
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  • #2
Hello IADPCFEVER,

Consider $(x_0.y_0)\in\mathbb{R}^2$. The perpendicular line to $r:y=x$ passing through $(x_0,y_0)$ is $s:y-y_0=-(x-x_0)$. The intersection point between $r$ and $s$ is:
$$\left \{ \begin{matrix}x-y=0\\x+y=x_0+y_0\end{matrix}\right.\Leftrightarrow\ldots\Leftrightarrow(x,y)=\left(\dfrac{x_0+y_0}{2},\dfrac{x_0+y_0}{2}\right)$$
That is, if $p:\mathbb{R}^2\to \mathbb{R}^2$ projects $(x_0,y_0)$ onto $r:y=x$ then,
$$p\begin{pmatrix}{x_0}\\{y_0}\end{pmatrix}= \dfrac{1}{2} \begin{pmatrix}{x_0+y_0}\\{x_0+y_0}\end{pmatrix}= \dfrac{1}{2} \begin{pmatrix}{1}&{1}\\{1}&{1}\end{pmatrix}\begin{pmatrix}{x_0}\\{y_0}\end{pmatrix}=A \begin{pmatrix}{x_0}\\{y_0}\end{pmatrix}$$
Now, we easily prove that $p$ is a linear map. For all $\lambda,\mu\in\mathbb{R}$ and for all $v=(x_0,y_0)^t$, $v'=(x'_0,y'_0)^t$ in $\mathbb{R}^2$:
$$p(\lambda v+\mu v')=A(\lambda v+\mu v')=\lambda Av+\mu Av'=\lambda p(v)+\mu p(v')$$
 

Related to IADPCFEVER's question at Yahoo Answers (projection and linear transformation)

1. What is projection and linear transformation?

Projection and linear transformation are mathematical techniques used to map one set of data onto another set of data. They are often used in fields such as computer graphics, engineering, and statistics to simplify complex data and reveal patterns or relationships.

2. How are projection and linear transformation different?

Projection and linear transformation are similar in that they both involve mapping one set of data onto another. However, projection involves projecting one set of data onto a lower-dimensional space, while linear transformation involves transforming the original set of data into a different set of data.

3. What are some applications of projection and linear transformation?

Projection and linear transformation have a wide range of applications, including image and signal processing, data compression, and machine learning. They are also used in geometric transformations, such as rotating or scaling objects in computer graphics.

4. What are some common examples of projection and linear transformation in everyday life?

Some examples of projection and linear transformation in everyday life include converting Celsius to Fahrenheit, converting currency rates, and transforming a 2D image into a 3D model. These techniques are also used in GPS navigation, where coordinates from a 3D map are projected onto a 2D screen for easier navigation.

5. Are there any limitations to using projection and linear transformation?

While projection and linear transformation are powerful tools, they do have limitations. For example, projection can lead to loss of information and distortion of the original data, while linear transformation may not accurately represent the original data. It is important to carefully consider the data and the desired outcomes before using these techniques.

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