Linear operator in 2x2 complex vector space

In summary, the rank of T is 4. T2 is the complex conjugate of T. T2 is a linear operator on C2x2 that maps each column of A to the complex conjugate of that column in B.
  • #1
jolly_math
51
5
Homework Statement
see question below
Relevant Equations
basis
Let C2x2 be the complex vector space of 2x2 matrices with complex entries. Let
1664844975842.png
and let T be the linear operator onC2x2 defined by T(A) = BA. What is the rank of T? Can you describe T2?
____________________________________________________________

An ordered basis for C2x2 is:
1664845269015.png


I don't understand the following statement:
If we identify C2x2 with C4, then since
1664845520809.png
, the matrix of the transformation is
1664845390200.png
.

Could anyone explain this? Thank you.
 
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  • #2
Do you know how to compute ##T(A_{11})##?
 
  • #3
jolly_math said:
Could anyone explain this?
Which part specifically?

I think the 4x4 matrix should actually be the transpose of what you posted, but I might be missing something.
 
  • #4
vela said:
I think the 4x4 matrix should actually be the transpose of what you posted, but I might be missing something.
This depends on if we consider representing the ##\mathbb C^4## space as row or column vectors and is therefore up to convention. Without knowing the convention used we cannot say if it should be transposed or not.

What should be clear is that the operator ##B## acts separately on each column of ##A## and therefore can be decomposed into its separate action on ##\mathbb C^2\oplus \mathbb C^2## as ##T(A) = T(a_1\oplus a_2)= (B a_1)\oplus(Ba_2)##. The question therefore can be handled by considering the rank of ##B## when acting on ##\mathbb C^2##.
 
  • #5
Office_Shredder said:
Do you know how to compute ##T(A_{11})##?
No, I don't. I mainly don't understand how
1664985151442.png
was created, and why this results in
1664985186104.png
- I think there is more specific mathematical reasoning used that I can't see here. Thanks.
 
  • #6
jolly_math said:
No, I don't. I mainly don't understand how View attachment 315063 was created, and why this results in View attachment 315064 - I think there is more specific mathematical reasoning used that I can't see here. Thanks.
If ##T(A) = BA##, what is ##T(A_{11})##? (Explicitly for the given ##A_{11}## and ##B##)
 
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  • #7
Orodruin said:
If ##T(A) = BA##, what is ##T(A_{11})##? (Explicitly for the given ##A_{11}## and ##B##)
$$
\begin{bmatrix}
1 & 0 \\
-4 & 0 \\
\end{bmatrix}
$$. I think I understand how
1664985722146.png
was created now. How does this lead to
1664985770392.png
? Thanks.
 

Attachments

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  • #8
What matrix do you need to multiply the vector
$$
\begin{pmatrix} A_{11} \\ A_{21} \\ A_{12} \\ A_{22}\end{pmatrix}
$$
with to get the vector
$$
\begin{pmatrix}A_{11} - 4 A_{21} \\ -A_{11} + 4A_{21} \\ A_{12} - 4A_{22} \\ - A_{12} + 4A_{22}\end{pmatrix}
$$
?
 
  • #9
Orodruin said:
What matrix do you need to multiply the vector
$$
\begin{pmatrix} A_{11} \\ A_{21} \\ A_{12} \\ A_{22}\end{pmatrix}
$$
with to get the vector
$$
\begin{pmatrix}A_{11} - 4 A_{21} \\ -A_{11} + 4A_{21} \\ A_{12} - 4A_{22} \\ - A_{12} + 4A_{22}\end{pmatrix}
$$
?
I think it's a 1x4 matrix, but I can't think of a specific one.
 

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  • #10
jolly_math said:
I think it's a 1x4 matrix, but I can't think of a specific one.
No. You want to map a column vector with 4 entries to another column vector with 4 entries, which type of matrix do you need to use?
 
  • #11
Orodruin said:
No. You want to map a column vector with 4 entries to another column vector with 4 entries, which type of matrix do you need to use?
A 4x4 matrix?
 
  • #12
jolly_math said:
A 4x4 matrix?
Why? Can you motivate this answer?
 
  • #13
Orodruin said:
Why? Can you motivate this answer?
I'm actually not sure why. Could you please explain how
1664992793856.png
was created? I can't seem to understand it even with hints. Thanks.
 
  • #14
jolly_math said:
I'm actually not sure why. Could you please explain how View attachment 315074 was created? I can't seem to understand it even with hints. Thanks.
So let us go back to the basics. For a 2x2 matrix
$$
A = \begin{pmatrix} a & b \\ c & d\end{pmatrix}
$$
and a column vector
$$
X = \begin{pmatrix} x \\ y \end{pmatrix},
$$
what is ##AX##?
 
  • #15
Orodruin said:
So let us go back to the basics. For a 2x2 matrix
$$
A = \begin{pmatrix} a & b \\ c & d\end{pmatrix}
$$
and a column vector
$$
X = \begin{pmatrix} x \\ y \end{pmatrix},
$$
what is ##AX##?
AX = \begin{pmatrix} ax+by \\ cx+dy\end{pmatrix}
 
  • #16
Right, so what is
$$
\begin{pmatrix}T_{11} & T_{12} & T_{13} & T_{14} \\T_{21} & T_{22} & T_{23} & T_{24} \\T_{31} & T_{32} & T_{33} & T_{34} \\T_{41} & T_{42} & T_{43} & T_{44} \end{pmatrix}
\begin{pmatrix} A_{11} \\ A_{21} \\ A_{12} \\ A_{22}\end{pmatrix}
$$
?
 
  • #17
jolly_math said:
I'm actually not sure why. Could you please explain how View attachment 315074 was created? I can't seem to understand it even with hints. Thanks.
There are (at least) two ways to look at ##2 \times 2## matrices. You can leave the entries in matrix form. Or, you can represent each matrix as a 4D vector. The text makes the following correspondence:
$$
A = \begin{bmatrix} a & b \\ c & d\end{bmatrix} \leftrightarrow (a, b, c, d)
$$This also gives us a correspondence between two reprsentations of the basis matrices/vectors:
$$
A_{11} = \begin{bmatrix} 1 & 0 \\ 0 & 0\end{bmatrix} \leftrightarrow (1, 0, 0, 0) = e_1
$$etc.
Now, take a linear transformation ##T## represented by the matrix
$$
M = \begin{bmatrix} 1 & -1 \\ -4 & 4\end{bmatrix}
$$We can find the action of ##T## on a matrix by matrix multiplication:
$$T(A) = MA$$And, we can find the action on each basis matrix:
$$T(A_{11}) = MA_{11} = A_{11} - 4A_{12}$$etc.
We can also write down the action of ##T## as a linear transformation of our basis vectors ##e_1## etc.
$$Te_1 = e_1 - 4e_2$$etc.
Finally, we can express ##T## as a linear transformation on the vector representation by a ##4 \times 4## matrix. And that is the matrix that so mysteriously appeared in the text.
 
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  • #18
Orodruin said:
Right, so what is
$$
\begin{pmatrix}T_{11} & T_{12} & T_{13} & T_{14} \\T_{21} & T_{22} & T_{23} & T_{24} \\T_{31} & T_{32} & T_{33} & T_{34} \\T_{41} & T_{42} & T_{43} & T_{44} \end{pmatrix}
\begin{pmatrix} A_{11} \\ A_{21} \\ A_{12} \\ A_{22}\end{pmatrix}
$$
?
I was working on my reply when you posted this. I did feel the OP was struggling fundamentally with the concept of representing a ##2 \times 2## matrix by a 4D vector.
 
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  • #19
Thank you so much, it makes sense now!
 
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FAQ: Linear operator in 2x2 complex vector space

What is a linear operator in a 2x2 complex vector space?

A linear operator in a 2x2 complex vector space is a mathematical function that maps a vector from the space onto itself, while preserving the vector's linear properties. This means that the output of the function is always a linear combination of the input vector.

How is a linear operator represented in a 2x2 complex vector space?

A linear operator in a 2x2 complex vector space can be represented by a 2x2 matrix, where each element of the matrix corresponds to a specific transformation of the vector elements. The matrix representation of a linear operator is also known as its standard form.

What are the properties of a linear operator in a 2x2 complex vector space?

Some key properties of a linear operator in a 2x2 complex vector space include linearity, which means that the function preserves vector addition and scalar multiplication, and invertibility, which means that the function has an inverse that can be used to reverse its transformation.

How is the action of a linear operator determined in a 2x2 complex vector space?

The action of a linear operator in a 2x2 complex vector space is determined by multiplying the operator's matrix representation with the vector that it is acting upon. This results in a new vector that has been transformed by the linear operator.

What are some real-world applications of linear operators in 2x2 complex vector spaces?

Linear operators in 2x2 complex vector spaces have many applications in physics, engineering, and computer science. They are commonly used to model physical systems, such as quantum mechanics and electrical circuits, and to perform transformations in computer graphics and image processing.

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