Constructing a matrix version of the transformation algorithm?

In summary, the transformation algorithm is not written in matrix form, and it probably converts the graph of the function into the graph of the matrix.
  • #1
eleventhxhour
74
0
Algorithms like the transformation algorithm: $(x, y)$ --> $(\frac{x}{k} + p, ay + d)$ are not generally used in mathematics. Instead, we use matrices.

Multiplying matrixes: you multiply a row of the first matrix by a column of the second. Use the following example:

$ \begin{bmatrix}x & y \end{bmatrix}\begin{bmatrix}a & b \\ c & d \end{bmatrix} = \begin{bmatrix}ax + cy & bx + dy \end{bmatrix} $

Use this information to construct a matrix version of the transformation algorithm for the transformation $y=af[k(x−p)]+d$
 
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  • #2
eleventhxhour said:
Algorithms like the transformation algorithm: $(x, y)$ --> $(\frac{x}{k} + p, ay + d)$ are not generally used in mathematics. Instead, we use matrices.

Multiplying matrixes: you multiply a row of the first matrix by a column of the second. Use the following example:

$ \begin{bmatrix}x & y \end{bmatrix}\begin{bmatrix}a & b \\ c & d \end{bmatrix} = \begin{bmatrix}ax + cy & bx + dy \end{bmatrix} $

Use this information to construct a matrix version of the transformation algorithm for the transformation $y=af[k(x−p)]+d$

Welcome to MHB, eleventhxhour!

Usually matrices are used if you have at least 2 variables (typically x and y) that are transformed in 2 new variables.

That is not the case for your transformation, so it makes little sense to write it in matrix notation.

Anyway, if you really want to, it could be written with 1x1 matrices as:
$$\begin{bmatrix} y \end{bmatrix}
= \begin{bmatrix} x−p \end{bmatrix} \begin{bmatrix} afk \end{bmatrix} + \begin{bmatrix} d \end{bmatrix}$$
or
$$\begin{bmatrix} y \end{bmatrix}
= \begin{bmatrix} x \end{bmatrix} \begin{bmatrix} afk \end{bmatrix} + \begin{bmatrix} d -afkp \end{bmatrix}
$$

Or a more advanced form:
$$\begin{bmatrix} y \end{bmatrix} = \begin{bmatrix} x & 1 \end{bmatrix} \begin{bmatrix} afk \\ d - afkp \end{bmatrix}$$
 
  • #3
eleventhxhour said:
Use this information to construct a matrix version of the transformation algorithm for the transformation $y=af[k(x−p)]+d$
I suspect that $f$ is a function here. Let $g(x)=af\big(k(x-p)\big)+d$. Then the required transformation probably converts the graph of $f(x)$ into the graph of $g(x)$. OP, please confirm if this is the correct interpretation.
 
  • #4
Evgeny.Makarov said:
I suspect that $f$ is a function here. Let $g(x)=af\big(k(x-p)\big)+d$. Then the required transformation probably converts the graph of $f(x)$ into the graph of $g(x)$. OP, please confirm if this is the correct interpretation.

Yes, I'm pretty sure that's the correct interpretation.
 
  • #5
eleventhxhour said:
Yes, I'm pretty sure that's the correct interpretation.

What is the definition of $f$ then?
Is it perhaps related to your transformation algorithm: $(x, y) \to(\frac{x}{k} + p, ay + d)$?
It seems to fit.
 
  • #6
I like Serena said:
What is the definition of $f$ then?
Is it perhaps related to your transformation algorithm: $(x, y) \to(\frac{x}{k} + p, ay + d)$?
It seems to fit.
Yes, this transformation maps the graph of $f(x)$ into that of $g(x)$. It cannot be represented in the form
\[
(x',y')=(x,y)A\qquad(*)
\]
for a 2x2 matrix A. Indeed, (*) maps $(0,0)$ to $(0,0)$, while the transformation in the quote maps $(0,0)$ to $(p,d)$. But note that
\[
(x,y,1)
\begin{pmatrix}
a_{11}&a_{12}&0\\
a_{21}&a_{22}&0\\
a_{1}&a_{b2}&1
\end{pmatrix}
=(a_{11}x+a_{21}y+b_1, a_{12}x+a_{22}y+b_2,1)
\]
You need to come up with a matrix that produces
\[
((1/k)x +0y + p, 0x+ay + d,1)
\]
 
Last edited:
  • #7
Evgeny.Makarov said:
Yes, this transformation maps the graph of $f(x)$ into that of $g(x)$. It cannot be represented in the form
\[
(x',y')=(x,y)A\qquad(*)
\]
for a 2x2 matrix A. Indeed, (*) maps $(0,0)$ to $(0,0)$, while the transformation in the quote maps $(0,0)$ to $(p,d)$. But note that
\[
(x,y,1)
\begin{pmatrix}
a_{11}&a_{12}&0\\
a_{21}&a_{22}&0\\
a_{1}&a_{b2}&1
\end{pmatrix}
=a_{11}x+a_{21}y+b_1, a_{12}x+a_{22}y+b_2,1)
\]
You need to come up with a matrix that produces
\[
((1/k)x +0y + p, 0x+ay + d,1)
\]

Hmm..okay, I think I understand that a bit more. How would you create that matrix?
 
  • #8
eleventhxhour said:
How would you create that matrix?
You compare the two expressions
\[
(a_{11}x+a_{21}y+b_1, a_{12}x+a_{22}y+b_2,1)
\]
and
\[
((1/k)x +0y + p, 0x+ay + d,1)
\]
and come up with appropriate $a_{ij}$ and $b_i$ so that the expressions coincide.
 
  • #9
Hi,
I hope the following discussion helps:

33v0x2o.png
 

FAQ: Constructing a matrix version of the transformation algorithm?

1. What is a matrix version of the transformation algorithm?

A matrix version of the transformation algorithm is a method used to convert a set of data points into a matrix form. This allows for easier manipulation and analysis of the data.

2. Why would you want to construct a matrix version of the transformation algorithm?

Constructing a matrix version of the transformation algorithm can make it easier to perform calculations on large sets of data. It can also help to simplify complex data sets and make them easier to interpret.

3. What are the steps involved in constructing a matrix version of the transformation algorithm?

The first step is to identify the data points and determine the appropriate dimensions for the matrix. Then, the data points are mapped onto the matrix and any necessary transformations are applied. Finally, the matrix is analyzed and interpreted to draw conclusions from the data.

4. Are there any limitations to using a matrix version of the transformation algorithm?

While constructing a matrix version of the transformation algorithm can be useful, it may not always be the most efficient or accurate method for analyzing data. It may also be limited by the complexity of the data or the available computing resources.

5. Can the matrix version of the transformation algorithm be applied to any type of data?

Yes, the matrix version of the transformation algorithm can be applied to a wide range of data types, including numerical, categorical, and even textual data. However, the approach may need to be adapted depending on the specific type of data being analyzed.

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