Isomorphic Finite Dimensional Vector Spaces

In summary, the conversation discusses the concept of isomorphic vector spaces and their relationship to having the same dimension. The participants also touch on assumptions and clarifications about the operations and fields involved in this theory. The conversation ends with the suggestion that the book is a good resource for self-study.
  • #1
TheOldHag
44
3
I'm going through the text "Linear Algebra Done Right" 2nd edition by Axler. Made it to chapter 4 with one problem I'm unable to understand fully. The theory that two vector spaces are isomorphic if and only if they have the same dimension. I can see this easily in one direction, that is, isomorphic vector spaces will have the same dimension, but it seems I can imagine vector spaces having the same dimension but for which they are not isomorphic. Perhaps one vector space would be 2-tuples over the integers but the with modular operators limiting the number of elements in the space and another vector space of integer 2-tuples without modular operators. The latter space would have more element in it so how would any map from the former to the latter be surjective? And yet do they not have the same dimension?

I'm guessing he is making assumptions about the operations on the vector spaces and they are over R or C. However, this assumption is made on many of the theories this particular theory doesn't state V explicitly and so I'm not sure what part of this theory the assumptions fall under.

Any clarification on this would be appreciated.
 
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  • #2
Let V and W be n-dimensional vector spaces over the same base field F. Fix bases {e1,...,en} and {f1,...,fn} for V and W respectively. Define a linear transformation φ:V→W by setting φek = fk and extending by linearity. This is an isomorphism.
 
  • #3
TheOldHag said:
. Perhaps one vector space would be 2-tuples over the integers but the with modular operators limiting the number of elements in the space and another vector space of integer 2-tuples without modular operators. The latter space would have more element in it so how would any map from the former to the latter be surjective? And yet do they not have the same dimension?

I'm guessing he is making assumptions about the operations on the vector spaces and they are over R or C. However, this assumption is made on many of the theories this particular theory doesn't state V explicitly and so I'm not sure what part of this theory the assumptions fall under.

Any clarification on this would be appreciated.

It's a vector space, so it's a vector space over a field.. R, C, Q or any other random field will work. Both vector spaces have to be over the same field but that's the only requirement - note your attempt at a counterexample is not a vector space because there is no field of scalars ( well, when you mod out if it's by a prime number there is)
 
  • #4
That answers it for me. Basically, I haven't gone over abstract algebra yet so I think in this case I just have to move forward and assume the notion of field will clear up that detail later. Thanks.
 
  • #5
TheOldHag said:
That answers it for me. Basically, I haven't gone over abstract algebra yet so I think in this case I just have to move forward and assume the notion of field will clear up that detail later. Thanks.

Since Axler apparently does not introduce abstract fields, just read my post again with the first line changed to: "Let V and W be n-dimensional real or complex vector spaces." The argument is the same from there.
 
  • #6
Your post has gotten me thinking about the very definition of liner map. I do better with more rigorous text only because they don't omit tedious and trivial details. For instance, he defines a linear map as having homogeneity and additivity and leaves it to the reader to deduce that V and W have to be vector spaces over the same field by the fact that the constant 'a' appears in both side of the equation of homogeneity. Yes, it should be obvious but I do better when it is spelled out. Perhaps it would have been more clear if he qualified that a linear map was a function between vector spaces over the same field.

Nevertheless at the beginning of each chapter he states the assumption that F is R or C and that V is a vector space over F. However, the theorem I was confused about didn't mention V or F at all.

From what I can tell though, this is a great book for self study.
 

1. What is an isomorphic finite dimensional vector space?

An isomorphic finite dimensional vector space is a mathematical concept that describes two vector spaces that have the same structure and can be mapped onto each other in a one-to-one and onto manner. This means that they have the same number of dimensions and the same operations can be performed on their vectors.

2. How do you prove that two vector spaces are isomorphic?

In order to prove that two vector spaces are isomorphic, you need to show that there exists a bijective linear transformation between them. This means that the transformation preserves the vector space structure, such as addition and scalar multiplication, and is both one-to-one and onto.

3. What are the benefits of studying isomorphic finite dimensional vector spaces?

Studying isomorphic finite dimensional vector spaces can help to deepen our understanding of vector spaces and linear algebra. It also allows us to simplify complex mathematical problems by transforming them into simpler vector spaces that are isomorphic to the original ones.

4. Can two vector spaces be isomorphic if they have different bases?

Yes, two vector spaces can still be isomorphic even if they have different bases. This is because the basis of a vector space is not a defining factor of its isomorphism. As long as the two vector spaces have the same number of dimensions and the same operations can be performed on their vectors, they can be considered isomorphic.

5. Are isomorphic finite dimensional vector spaces unique?

No, isomorphic finite dimensional vector spaces are not unique. There can be multiple isomorphisms between two vector spaces, and the choice of which isomorphism to use is not unique. However, the two vector spaces themselves must have the same number of dimensions and the same operations in order to be considered isomorphic.

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