Linear Transformations - formula

In summary, the given equations can be rewritten as a matrix equation and solved for the matrix A by using matrix multiplication. This is possible because the last matrix is equivalent to the three individual equations.
  • #1
notmuch
16
0
Hello. I am given the following:

T([1,2,-3]) = [1,0,4,2]
T([3,5,2]) = [-8,3,0,1]
T([-2,-3,-4]) = [0,2,-1,0]

And of course I know that:

T(x) = Ax

and I want to find the matrix A.

So, from the individual equations, I construct:

A[1, 2, -3] = [1, 0, 4, 2] (please forgive, these are actually col. vectors)

I do something similar for the other two, and come up with the equation below. I can solve this equation to obtain (the correct) matrix A, but I can't seem to find an explanation for why it is possible to throw it all together into "combined matrices." Could anyone help? Thanks!

[tex]
A\begin{pmatrix}
1 & 3 & -2\\
2 & 5 & -3\\
-3 & 2 & -4\end{pmatrix} =
\begin{pmatrix}
1 & -8 & 0\\
0 & 3 & 2\\
4 & 0 & -1\\
2 & 1 & 0\end{pmatrix}
[/tex]
 
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  • #2
Because the last matrix is equivalent to the three individual equations. This just follows from matrix multiplication. Schematically, if the v_n's are column vectors, you can write:

[tex]A\left(
\begin{array}{cccc}
\vert & \vert & \vert & \vdots \\
v_1 & v_2 & v_3 & \vdots \\
\vert & \vert & \vert & \vdots \\
\end{array}\right)=\left(
\begin{array}{cccc}
\vert & \vert & \vert & \vdots \\
Av_1 & Av_2 & Av_3 & \vdots \\
\vert & \vert & \vert & \vdots \\
\end{array}\right)[/tex]
 
  • #3
Got it. Thanks a lot!
 

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another, while preserving the properties of addition and scalar multiplication. In other words, it is a transformation that maintains the straightness of lines and the origin point.

2. What is the formula for a linear transformation?

The general formula for a linear transformation is T(x) = Ax + b, where T is the transformation, A is the transformation matrix, x is the input vector, and b is the translation vector. This formula can also be written in terms of matrix multiplication as T(x) = A * x + b.

3. How do you determine if a transformation is linear?

A transformation is linear if it satisfies two properties: additivity and homogeneity. Additivity means that T(u + v) = T(u) + T(v), where u and v are input vectors. Homogeneity means that T(cu) = cT(u), where c is a scalar. If a transformation satisfies both of these properties, it is considered linear.

4. What is the role of the transformation matrix in a linear transformation?

The transformation matrix is a crucial component in a linear transformation as it represents the effects of the transformation on the input vector. It determines the scaling, rotation, and shearing of the input vector, while the translation vector determines the location of the transformed vector in relation to the origin.

5. How are linear transformations used in real-world applications?

Linear transformations have a wide range of applications in fields such as physics, engineering, computer graphics, and economics. They are used to model and analyze physical systems, transform data in computer graphics, and solve optimization problems in economics. They also have practical applications in image and signal processing, data compression, and machine learning.

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