Linear transformations question

In summary, Santiago found a matrix that describes the linear transformation from β_1 to β_2. This matrix is independent of the basis vectors used, so proving that T is injective is easy.
  • #1
Rackhir
17
0

Homework Statement


Today in my final i was given this exercise:
Given [itex]β_1=\{v_1,v_2,v_3\}[/itex] and [itex]β_2=\{u_1,u_2,u_3,u_4\}[/itex], basis of the vector spaces [itex]V[/itex] and [itex]U[/itex] respectively.
a) Find the linear transformation [itex]T:U\rightarrow V[/itex] so that [itex]T(v_i)≠T(v_j)[/itex] if [itex]i≠j[/itex], [itex]T(v_1)=u_1+u_2[/itex] and [itex]T[/itex] is injective

b) Find the transformation matrix from [itex]β_1[/itex] to [itex]β_2[/itex], [itex][T]_{β_1 \rightarrow β_2}[/itex]

Homework Equations


If [itex]T[/itex] is injective if and only if [itex]Kernel(T)=\{0\}[/itex], that means that the nullspace of the transformation matrix is [itex]\{0\}[/itex]

The Attempt at a Solution


I thought that finding [itex][T]_{β_1 \rightarrow β_2}[/itex] first would be easier, or at least it made more sense for me. I found this matrix
[tex] \begin{pmatrix}
1&0&0\\
1&0&0\\
0&1&0\\
0&0&1\\
\end{pmatrix} [/tex]

Where the first column, [itex]\begin{pmatrix}
1\\
1\\
0\\
0\\
\end{pmatrix}[/itex]
comes from [itex]T(v_1)=u_1+u_2[/itex], and the other two were chosen so the nullspace of [itex][T]_{β_1 \rightarrow β_2}[/itex] is in fact, [itex]\{0\}[/itex]
My big question, is this right? or it's horribly wrong and i should feel ashamed when i look myself in the mirror?

Then, for a) find the transformation per se, should i solve the following system?
[tex] \begin{pmatrix}
1&0&0\\
1&0&0\\
0&1&0\\
0&0&1\\
\end{pmatrix} \begin{pmatrix}x\\y\\z\end{pmatrix} = \begin{pmatrix}a\\b\\c\\d\end{pmatrix}[/tex], where [itex](x,y,z) \in V[/itex] and [itex](a,b,c,d) \in U[/itex]. So i end with [itex] a=x, b=x, c=y, d=z [/itex], but this is highly dependant on the basis chosen, right? shouldn't be independant?

Any help will be highly appreciated, i'd love to know how this is actually solved.
 
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  • #2
Your matrix is fine. Feel free to look in the mirror.

For part (a), all you probably needed to do was say what T does to the basis vectors in β1, and then prove that T is injective.
 
  • #3
vela said:
Your matrix is fine. Feel free to look in the mirror.

For part (a), all you probably needed to do was say what T does to the basis vectors in β1, and then prove that T is injective.

YEY, i feel relieved :) and what do you mean with "all you probably needed to do was say what T does to the basis vectors in β1"? is [itex]a=x, b=x, c=y, d=z \ \forall \ (x,y,z)∈V \ and \ (a,b,c,d)∈U[/itex] wrong? as you can see, i have more problems finding out what i need to do, rather than doing it
I just hope my teachers dion't mark me wrong because i found the matrix first :P
Santiago
 
  • #4
Like you said, the calculation you did depends on the basis, but saying that T(v1) = u1+u2 is independent of the basis. Whatever representation you use, T will always map v1 to u1+u2.

Because T is linear, if you specify what it does to a set of basis vectors, you've determined what T will do to any vector in the space, so simply saying what T(v1), T(v2), and T(v3) are is enough to fully describe T.
 
  • #5
vela said:
Like you said, the calculation you did depends on the basis, but saying that T(v1) = u1+u2 is independent of the basis. Whatever representation you use, T will always map v1 to u1+u2.

Because T is linear, if you specify what it does to a set of basis vectors, you've determined what T will do to any vector in the space, so simply saying what T(v1), T(v2), and T(v3) are is enough to fully describe T.

Oh i see, so, with the matrix i chose, [itex]T(v_1)=u_1+u_2[/itex], [itex]T(v_2)=u_3[/itex] and [itex]T(v_3)=u_4[/itex]. Well that does make sense now. And since they will be clearly linearly independant, the proof that T is injective is rather obvious.
 

FAQ: Linear transformations question

What is a linear transformation?

A linear transformation is a function that maps one vector space to another in a way that preserves the properties of vector addition and scalar multiplication. In other words, it transforms one set of coordinates into another set of coordinates while maintaining the same underlying structure.

How is a linear transformation represented?

A linear transformation can be represented by a matrix. The columns of the matrix represent the images of the basis vectors of the original vector space.

What is the difference between a linear transformation and a nonlinear transformation?

A linear transformation follows the rules of linearity, meaning that the output is directly proportional to the input. A nonlinear transformation, on the other hand, does not follow these rules and may result in a curved or non-proportional output.

What is the purpose of studying linear transformations?

Linear transformations have many applications in mathematics, physics, engineering, and other fields. They are used to model real-world phenomena, solve systems of equations, and analyze geometric shapes and patterns.

How are linear transformations applied in data analysis and machine learning?

In data analysis and machine learning, linear transformations are used to transform and manipulate data in order to better understand patterns and relationships. They are also used in various algorithms for tasks like dimensionality reduction and feature extraction.

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