Is the Proof for Same Dimension and Isomorphism Correct?

  • Thread starter TranscendArcu
  • Start date
  • Tags
    Isomorphism
In summary, the proof shows that if V, W are finite dimensional vector spaces that are isomorphic, then V, W have the same dimension. This is proven by showing that T is linear when T(aX + bY) = T(ax_1 + by_1,ax_2 + by_2,ax_3 + by_3) = (ax_2 + by_2,0,ax_3 + by_3) = (ax_2,0,ax_3) + (by_2,0,by_3) = a(x_2,0,x_3) + b(y_2,0,y_3) = aT(X) + bT(Y),
  • #1
TranscendArcu
285
0

Homework Statement


A theorem in my book states: If V, W are finite dimensional vector spaces that are isomorphic, then V, W have the same dimension. I wrote a proof but it is different from the proof given in my book, and I'd like to know if it's right.

The Attempt at a Solution


Let [itex]\left\{A_1, ...,A_n\right\}[/itex] be a basis for V. Because V,W are isomorphic, we know [itex]ImT = W[/itex], which implies that [itex]dim(ImT) = dim(W)[/itex]. We also know [itex]KerT = \left\{ 0 \right\}[/itex] and hence [itex]dim(KerT) = 0[/itex] We know [itex]dim(V) = n = dim(ImT) + dim(KerT)= dim(ImT) + 0 = dim(ImT)[/itex]. Thus, [itex]dim(ImT) = n = dim(W)[/itex]

Is that right?
 
Physics news on Phys.org
  • #2
TranscendArcu said:

Homework Statement


A theorem in my book states: If V, W are finite dimensional vector spaces that are isomorphic, then V, W have the same dimension. I wrote a proof but it is different from the proof given in my book, and I'd like to know if it's right.



The Attempt at a Solution


Let [itex]\left\{A_1, ...,A_n\right\}[/itex] be a basis for V. Because V,W are isomorphic, we know [itex]ImT = W[/itex], which implies that [itex]dim(ImT) = dim(W)[/itex]. We also know [itex]KerT = \left\{ 0 \right\}[/itex] and hence [itex]dim(KerT) = 0[/itex] We know [itex]dim(V) = n = dim(ImT) + dim(KerT)= dim(ImT) + 0 = dim(ImT)[/itex]. Thus, [itex]dim(ImT) = n = dim(W)[/itex]

Is that right?

This is the proof in my book
 
  • #3
This is the massive proof in my text. It's seems so unintuitive to me.

http://desmond.imageshack.us/Himg696/scaled.php?server=696&filename=screenshot20120202at557.png&res=medium
http://desmond.imageshack.us/Himg832/scaled.php?server=832&filename=screenshot20120202at558.png&res=medium
http://desmond.imageshack.us/Himg215/scaled.php?server=215&filename=screenshot20120202at558.png&res=medium

I mean, seriously.
 
Last edited by a moderator:
  • #4
TranscendArcu said:
This is the massive proof in my text. It's seems so unintuitive to me.

http://desmond.imageshack.us/Himg696/scaled.php?server=696&filename=screenshot20120202at557.png&res=medium
http://desmond.imageshack.us/Himg832/scaled.php?server=832&filename=screenshot20120202at558.png&res=medium
http://desmond.imageshack.us/Himg215/scaled.php?server=215&filename=screenshot20120202at558.png&res=medium

I mean, seriously.

My book is written by a physicist, for non-mathematicians, (explicitly stated in the book title)
 
Last edited by a moderator:
  • #5
TranscendArcu said:
This is the massive proof in my text. It's seems so unintuitive to me.
What aspect of it do you find unintuitive? It seems straightforward and a natural way to proceed if you don't want to appeal to the rank-nullity theorem (which itself requires a non-trivial proof) as you did in your solution. Maybe the book hadn't proved the latter theorem yet?

Also, your text's proof gives some additional information. Instead of merely verifying that the two spaces have the same dimension, it shows you how to construct a basis for the target space.
 
  • #6
I'm just working some practice problems here and I'd like to get my work checked. In particular: http://desmond.imageshack.us/Himg638/scaled.php?server=638&filename=screenshot20120202at613.png&res=medium

Let [itex]X = (x_1,x_2,x_3), Y = (y_1,y_2,y_3) \in R^3[/itex]. Let [itex]a,b \in R[/itex]. [itex]T(aX + bY) = T(ax_1 + by_1,ax_2 + by_2,ax_3 + by_3) = (ax_2 + by_2,0,ax_3 + by_3) = (ax_2,0,ax_3) + (by_2,0,by_3) = a(x_2,0,x_3) + b(y_2,0,y_3) = aT(X) + bT(Y)[/itex]. Which shows that T is linear.

Vertors in [itex]KerT[/itex] have the form [itex](x,0,0)[/itex] where [itex]x \in R[/itex]. Thus, the standard basis will do for all vectors in the kernel. Namely [itex]\left\{(1,0,0)\right\}[/itex], which has dimension 1.

Vectors in [itex]ImT[/itex] have the form [itex](y,0,z)[/itex] where [itex]y,z \in R[/itex]. Again the standard basis will do for all vectors in kernel. Namely, [itex] \left\{(1,0,0),(0,0,1) \right\}[/itex], which has dimension 2.

Vectors in T(U) have the form [itex](0,0,z)[/itex] where [itex]z \in R[/itex]. This is just the z-axis and we can use [itex] \left\{ (0,0,1) \right\}[/itex] as a basis (which is dimension 1).
 
Last edited by a moderator:
  • #7
jbunniii said:
What aspect of it do you find unintuitive? It seems straightforward and a natural way to proceed if you don't want to appeal to the rank-nullity theorem (which itself requires a non-trivial proof) as you did in your solution. Maybe the book hadn't proved the latter theorem yet?

Also, your text's proof gives some additional information. Instead of merely verifying that the two spaces have the same dimension, it shows you how to construct a basis for the target space.

Maybe unintuitive was the wrong word. Personally, I'm not a fan of that proof. Partly because it's long, partly because I didn't think of it, and partly because it uses the fundamental definition for a basis (namely, that it spans and is linearly independent). I like my proof more because it is short and relies on the simple equation dimV = dimKerT + dimImT.
 
  • #8
TranscendArcu said:
Maybe unintuitive was the wrong word. Personally, I'm not a fan of that proof. Partly because it's long, partly because I didn't think of it, and partly because it uses the fundamental definition for a basis (namely, that it spans and is linearly independent). I like my proof more because it is short and relies on the simple equation dimV = dimKerT + dimImT.

Fair enough, but the equation dimV = dimKerT + dimImT requires a similar, even longer proof, so you have simply hidden the details by using that theorem. Nothing logically wrong with that, as long as "dimV = dimKerT + dimImT" has already been proved at this point.
 
  • #9
TranscendArcu said:
I'm just working some practice problems here and I'd like to get my work checked. In particular: http://desmond.imageshack.us/Himg638/scaled.php?server=638&filename=screenshot20120202at613.png&res=medium

Let [itex]X = (x_1,x_2,x_3), Y = (y_1,y_2,y_3) \in R^3[/itex]. Let [itex]a,b \in R[/itex]. [itex]T(aX + bY) = T(ax_1 + by_1,ax_2 + by_2,ax_3 + by_3) = (ax_2 + by_2,0,ax_3 + by_3) = (ax_2,0,ax_3) + (by_2,0,by_3) = a(x_2,0,x_3) + b(y_2,0,y_3) = aT(X) + bT(Y)[/itex]. Which shows that T is linear.

Vertors in [itex]KerT[/itex] have the form [itex](x,0,0)[/itex] where [itex]x \in R[/itex]. Thus, the standard basis will do for all vectors in the kernel. Namely [itex]\left\{(1,0,0)\right\}[/itex], which has dimension 1.

Vectors in [itex]ImT[/itex] have the form [itex](y,0,z)[/itex] where [itex]y,z \in R[/itex]. Again the standard basis will do for all vectors in kernel. Namely, [itex] \left\{(1,0,0),(0,0,1) \right\}[/itex], which has dimension 2.

Vectors in T(U) have the form [itex](0,0,z)[/itex] where [itex]z \in R[/itex]. This is just the z-axis and we can use [itex] \left\{ (0,0,1) \right\}[/itex] as a basis (which is dimension 1).

This all looks correct to me.
 
Last edited by a moderator:

What is the concept of "Same Dim." in scientific terms?

"Same Dim." refers to objects or systems that have the same number of dimensions. This means that they possess the same amount of measurable parameters or characteristics that describe their shape, size, or position in space.

What is an isomorphism and how is it related to "Same Dim."?

An isomorphism is a mathematical concept that describes a one-to-one correspondence between two mathematical structures. In the context of "Same Dim.", an isomorphism refers to the ability to map one object onto another object of the same dimensions, while preserving the structural relationships between their elements.

How is "Same Dim." and isomorphism used in scientific research?

"Same Dim." and isomorphism are commonly used in various scientific disciplines, such as physics, chemistry, and biology, to study and understand the structure and behavior of different systems. They help scientists compare and contrast different objects or systems, and make predictions based on the similarities and differences between them.

What are some real-world examples of "Same Dim." and isomorphism?

Some examples of "Same Dim." and isomorphism in the real world include mapping the structure of DNA onto the structure of a protein, comparing the shape and size of different cells in biology, and analyzing the structural similarities between different molecules in chemistry.

Are there any limitations to the concept of "Same Dim." and isomorphism?

While "Same Dim." and isomorphism are useful tools for understanding and comparing different systems, they do have some limitations. For example, they may not be applicable to systems that have complex or dynamic structures, or those that cannot be easily measured or quantified. Additionally, the concept of "Same Dim." may not always capture all of the important characteristics of a system, leading to incomplete or inaccurate comparisons.

Similar threads

  • Calculus and Beyond Homework Help
Replies
1
Views
613
  • Calculus and Beyond Homework Help
Replies
14
Views
602
  • Calculus and Beyond Homework Help
Replies
34
Views
2K
  • Calculus and Beyond Homework Help
Replies
1
Views
1K
  • Calculus and Beyond Homework Help
Replies
14
Views
3K
  • Linear and Abstract Algebra
Replies
2
Views
867
  • Calculus and Beyond Homework Help
Replies
5
Views
1K
  • Calculus and Beyond Homework Help
Replies
3
Views
2K
  • Calculus and Beyond Homework Help
Replies
1
Views
1K
  • Calculus and Beyond Homework Help
Replies
3
Views
1K
Back
Top