Showing that a linear transformation from P3 to R4 is an isomorphism?

In summary: By showing that T is both surjective and injective, you have proven that it is an isomorphism. This means that every vector in R4 can be reached by applying T to some vector in P3, and that T only maps one vector in P3 to each vector in R4. This shows that the image of T spans R4, since every vector in R4 is mapped to by T.
  • #1
elo
2
0
I have a linear transformation, T, from P3 (polynomials of degree ≤ 3) to R4 (4-dimensional real number space). I have a second linear transformation, U, from R4 back to P3.

In the first step of this four-step problem, I have shown that the composition TU from R4 to R4 is the identity linear transformation by sending a vector x through U, then sending the product of that through T, and ending up with x again.

What I don't understand is the next step: show that T is an isomorphism. The problem gives the hint that I should first show that the image of T spans R4: okay, fair enough. After some misguided attempts to prove linear independence of the entries of the column vector of T(f), I skipped ahead to the solutions, and was given this:

"Given a vector x in R4, we have T(U(x)) = x, by the previous part, and so x is a value of T, and im(T) = R4."

I do not understand this answer. I did T(U(x)) = x, so I get that. But what does it mean that "x is a value of T?" And how does that mean that the image of T spans R4?
 
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  • #2
If you have already shown that ##TU=I##, then ##T## is surjective: if not, then the range of ##T## would not be all of ##\mathbb{R}^4##, and so the range of ##TU## could not be either, contradicting ##TU=I##. Can you use the rank-nullity theorem to conclude that ##T## is also injective?
 
  • #3
elo said:
"Given a vector x in R4, we have T(U(x)) = x, by the previous part, and so x is a value of T, and im(T) = R4."

I do not understand this answer. I did T(U(x)) = x, so I get that. But what does it mean that "x is a value of T?" And how does that mean that the image of T spans R4?
I think it's a typo or misstatement. It would be correct if it said "and so x is an element of im(T)".
 
  • #4
Okay: since I can get a 4-dimensional x out of T, the image of T must span R4. The starting space P3 has four dimensions. By rank-nullity, the dimension of the kernel of the transformation plus the dimension of its image must equal the dimension of P3; since the dimension of the image is four, the dimension of the kernel must be zero. Thus, T is both surjective and injective, and is thus an isomorphism.

Do I have the right idea?
 
  • #5
elo said:
Okay: since I can get a 4-dimensional x out of T, the image of T must span R4. The starting space P3 has four dimensions. By rank-nullity, the dimension of the kernel of the transformation plus the dimension of its image must equal the dimension of P3; since the dimension of the image is four, the dimension of the kernel must be zero. Thus, T is both surjective and injective, and is thus an isomorphism.

Do I have the right idea?
Yes, it sounds right to me!
 

1. What is a linear transformation?

A linear transformation is a function that maps a vector space to another vector space while preserving its algebraic structure. It is characterized by two properties: additivity and homogeneity.

2. How do you prove that a linear transformation is an isomorphism?

To prove that a linear transformation is an isomorphism, you need to show that it is both injective and surjective. This means that the transformation preserves both linear independence and spanning properties of the vector spaces.

3. What is the significance of proving that a linear transformation is an isomorphism?

Proving that a linear transformation is an isomorphism demonstrates that it is a one-to-one mapping between two vector spaces, which ensures that no information is lost in the transformation. This is important in many mathematical and scientific applications.

4. What is the difference between an isomorphism and an inverse transformation?

An isomorphism is a one-to-one mapping between two vector spaces, while an inverse transformation is a function that "undoes" the original transformation. An inverse transformation may not necessarily be an isomorphism, as it may not preserve all of the properties of the original transformation.

5. Can a linear transformation from P3 to R4 be an isomorphism if the dimensions of the vector spaces are different?

No, a linear transformation cannot be an isomorphism if the dimensions of the vector spaces are different. An isomorphism requires the same number of elements in both vector spaces, so they must have the same dimension.

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