Find Invariant Lines of Matrix Transformation y=mx+c

In summary, the invariant lines for the given transformation with matrix \left(\begin{array}{cc} 0 & 1 \\ 1 & 0\end{array}\right) are lines in the form y=mx+c, where x=m(mx+c)+c and y=m(mx+c)+c. This can be understood by observing that the x value of the transformed point (y,x) remains the same as the original y value, and therefore the line must have the same x value as the 'old y value'. This can be expressed mathematically as x=m(mx+c)+c, which simplifies to y=mx+c.
  • #1
Gregg
459
0

Homework Statement



find in the form y= mx+c, the invariant lines of the tranformation with matrix

[itex]
\left(
\begin{array}{cc}
0 & 1 \\
1 & 0
\end{array}
\right)[/itex]

[itex]

\left(
\begin{array}{cc}
0 & 1 \\
1 & 0
\end{array}
\right)\left(
\begin{array}{c}
x \\
\text{mx}+c
\end{array}
\right)=\left(
\begin{array}{c}
\text{mx}+c \\
x
\end{array}
\right)

[/itex]

[itex] \Rightarrow x = m(mx+c)+c [/itex] Why?

I just don't understand how that is implied in the first place and I don't have a method of working out invariant lines in the form mx or mx+c!
 
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  • #2
Because you want a line whose x value will remain the same after undergoing the transformation.


So when you multiply the matrix by (x,y) you get (y,x). You then want your line to have the the x value of the 'old y value'

and if Y=MX+C

X= mx+c

so Y=M(mx+c) + C

(I used capital letters to explain it better even though, the capitals are the same as the common ones)
 
  • #3


The term "invariant lines" refers to lines that do not change when the transformation is applied. In other words, the points on these lines are mapped to themselves by the transformation. In the given matrix transformation, we are looking for lines that remain unchanged when multiplied by the matrix. Invariant lines can be found by solving for the points that satisfy the equation:

\left(
\begin{array}{cc}
0 & 1 \\
1 & 0
\end{array}
\right)\left(
\begin{array}{c}
x \\
\text{mx}+c
\end{array}
\right)=\left(
\begin{array}{c}
x \\
\text{mx}+c
\end{array}
\right)

This can be rewritten as:

\begin{align*}
0x + 1(\text{mx}+c) &= x \\
\text{mx} + c &= x \\
\Rightarrow x &= m(mx+c)+c
\end{align*}

This is the equation for the invariant lines in the form y=mx+c. To find these lines, you can use any method of solving linear equations, such as substitution or elimination. Alternatively, you can also graph the transformation and look for lines that remain unchanged after the transformation is applied.
 

Related to Find Invariant Lines of Matrix Transformation y=mx+c

1. What is a matrix transformation?

A matrix transformation is a mathematical operation that involves multiplying a matrix by another matrix or a vector. This process results in a new matrix or vector with different values.

2. How do you find invariant lines of a matrix transformation?

To find invariant lines of a matrix transformation, you need to solve the equation y=mx+c, where y is the output, m is the slope, and c is the y-intercept. The values of m and c will remain the same for all points on the invariant line.

3. What is the significance of invariant lines in matrix transformations?

Invariant lines are important because they represent lines that do not change under the transformation. This means that all points on the line will have the same output, making it easier to understand the effects of the matrix transformation.

4. Can you provide an example of finding invariant lines of a matrix transformation?

For example, let's say we have a matrix transformation represented by the matrix A = [[2, 1], [4, 3]]. To find the invariant lines, we can set y=mx+c, where m and c are arbitrary values. Plugging in the values from the matrix, we get y=(2/1)x+c, which simplifies to y=2x+c. This means that all points on the line y=2x+c will have the same output after the transformation.

5. How can finding invariant lines be useful in real-world applications?

Invariant lines can be useful in fields such as computer graphics, engineering, and physics. They can help identify patterns and relationships in data, and make it easier to understand and manipulate complex transformations or systems.

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