About the isomorphism of 2 infinite-dimensional vector spaces

In summary, the conversation discusses finding a vector space V and decompositions V = A ⊕ B = C ⊕ D with A≈C but B and D are not isomorphic. It is mentioned that dim(V)=dim(A)+dim(B)=dim(C)+dim(D) and dim(A)=dim(C), but dim(B)≠dim(D) since V may not be finite-dimensional. A possible example for V is given using the set of all infinite ordered-tuples of real numbers with only finitely many nonzero entries, with A, B, C, and D being subspaces of V. It is noted that while A≈C, B and D are not isomorphic. Another example is suggested using different
  • #1
sanctifier
58
0
Notations:
V denotes a vector space
A, B, C, D denote subspaces of V respectively
≈ denotes the isomorphic relationship of the left and right operand
dim(?) denotes the dimension of "?"

Question:
Find a vector space V and decompositions:
V = A ⊕ B = C ⊕ D
with A≈C but B and D are not isomorphic.

My opinion:
dim(V)=dim(A)+dim(B)=dim(C)+dim(D) and dim(A)=dim(C), but dim(B)≠dim(D) since V may not be finite-dimensional. It's an idea not an example, would you make a concrete example of V?

Thanks for any help!
 
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  • #2
(Let ~ denote isomorphism, + a direct sum, and <S> the span of the set S. Sorry, but Latex seems to be out of commission.)

I think this works:

Let V be the set of all infinite ordered-tuples of real numbers with only finitely many nonzero entries, i.e., the set of all infinite sequences that eventually terminate, such as {3,2,1,0,0,...}. Let ei denote the sequence with a 1 in the ith place and zeros elsewhere. Then B = {e1,e2,...} is a basis for V over R. Let N1 = {e1} and N2 = {e1,e2}. Then V ~ V + <N1> ~ V + <N2> (I think). Clearly, <N1> is not isomorphic to <N2>, since their dimensions differ.
 
  • #3
VKint, I think the OP is looking for internal sums decompositions V = A ⊕ B = C ⊕ D.

But I think this slight variation on your idea works: take A:=<e_2,e_3,...>, B:=<e_1>, C:= <e_3,e_4,...>, D:=<e_1,e_2>.
 
  • #4
VKint,quasar987

Thanks!
 

1. What is an isomorphism?

An isomorphism is a mathematical concept that describes a one-to-one correspondence between two structures, such as vector spaces, that preserves their properties and operations. In simpler terms, it is a mapping between two structures that preserves their structure and behavior.

2. What are infinite-dimensional vector spaces?

Infinite-dimensional vector spaces are vector spaces that have an infinite number of basis vectors. This means that they have an infinite number of dimensions and can represent an infinite number of points or vectors in space.

3. How do you determine if two infinite-dimensional vector spaces are isomorphic?

To determine if two infinite-dimensional vector spaces are isomorphic, you must find a linear transformation between the two vector spaces that is both one-to-one and onto. This means that the transformation must map each vector in one space to a unique vector in the other space and that every vector in the second space is mapped to by at least one vector in the first space.

4. What is the significance of isomorphisms in mathematics?

Isomorphisms are important in mathematics because they allow us to understand and compare different structures that may seem unrelated at first. They also help us identify patterns and connections between different mathematical concepts, which can lead to new insights and discoveries.

5. Can two infinite-dimensional vector spaces be isomorphic if they have different bases?

Yes, two infinite-dimensional vector spaces can be isomorphic even if they have different bases. This is because the isomorphism is based on the structure and behavior of the vector space, not the specific elements or basis vectors used to represent it.

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