Is M_n(K) isomorphic to K \otimes_F M_n(F) as F-algebras?

In summary, the conversation discusses a problem involving tensor products and the isomorphism between M_n(K) and K \otimes_F M_n(F). The speakers suggest different approaches for proving this isomorphism, including using a ring homomorphism or showing isomorphism as F-modules. They also discuss the usefulness of considering all tensors rather than just simple ones and the importance of showing surjectivity.
  • #1
math_grl
49
0
So this is supposed be an introductory problem for tensor products that I was trying to do to verify I am understanding tensor products...turns out I'm not so much

Show that [tex]M_n(K)[/tex] is isomorphic as an [tex]F[/tex]-algebra to [tex]K \otimes_F M_n(F)[/tex] where [tex]F[/tex] is a field and [tex]K[/tex] is an extension field of [tex]F[/tex] and [tex]M_n(K)[/tex] means all the nxn matrices that have entries in K.

So I figure as F-algebras we need to show that we have a ring homomorphism that is linear (preserving the scalar multiplication) or showing they are isomorphic as F-modules (vec. sp's) then showing preservation of the multipication. Either way, my attempts are fruitless.
 
Physics news on Phys.org
  • #2
i think i solved it...nevermind
 
  • #3
actually i might have gotten ahead of myself...I don't think i have it.

I was trying [tex]\phi: K \otimes_F M_n(F) \rightarrow M_n(K)[/tex] where [tex]\phi (k \otimes A) = kA[/tex] as my mapping, then checking to see if it satisfied all the homomorphism conditions. This must be wrong tho.
 
  • #4
K tensor over F of the n dimensional F-vector space is isomorphic to the n dimensional K-vector space by easily proven properties of the tensor product over the direct sum. Perhaps this fact is useful?

I'm new to this, as well.
 
  • #5
TMM said:
K tensor over F of the n dimensional F-vector space is isomorphic to the n dimensional K-vector space by easily proven properties of the tensor product...

Sounds like a path to try but I would refrain from saying easily proven since we are not experts in this stuff yet...
 
  • #6
math_grl said:
Sounds like a path to try but I would refrain from saying easily proven since we are not experts in this stuff yet...

Fair enough. I think this would lead to a nicer proof, but I think your method works anyway.

You need to consider it acting on all the tensors in your product, not just the simple ones. Every tensor in the matrix group over K can be expressed as a sum of its values in each index. These can be decomposed into their magnitude and the matrix which has a 1 in the specific index and zero elsewhere. This matrix is in the matrix group over F so the map is surjective. If the images of two simple tensors are the same, you can cancel the factor in K, showing that the tensors are the same, so it is also injective.
 
  • #7
Thanks for the help. I noticed you mentioned that you said all tensor elements (finite sums of basis elements of the tensor product)...how come it doesn't suffice to just show it for the pure (simple) tensor elements since these span the tensor product as a vector space?
 
  • #8
Well it does, it's just easier to show surjectivity using a sum of simple tensors. In fact you need to since arbitrary elements of the codomain do not have preimages that are simple tensors.
 

1. What is a tensor product exercise?

A tensor product exercise is a mathematical exercise that involves finding the tensor product of two given vector spaces. It is a fundamental concept in linear algebra and is commonly used in various fields of science and engineering.

2. How do you compute a tensor product?

To compute a tensor product, you first need to find the basis vectors for each vector space. Then, you multiply each basis vector from one vector space with every basis vector from the other vector space, and write down the results in a table. Finally, you combine the results using the tensor product rule, which involves taking the outer product of the basis vectors and summing them up.

3. What is the purpose of tensor product exercises?

Tensor product exercises help in understanding the concept of tensor products and their applications in different fields. They also help in developing mathematical skills such as vector algebra and matrix operations.

4. Are there any real-world applications of tensor product exercises?

Yes, there are many real-world applications of tensor product exercises. For example, they are used in physics to describe the relationship between different physical quantities, in computer graphics and image processing for data compression and transformation, and in machine learning for dimensionality reduction and feature extraction.

5. What are some tips for solving tensor product exercises?

Some tips for solving tensor product exercises include being familiar with vector and matrix operations, understanding the tensor product rule, and practicing with different types of problems. It is also helpful to have a clear understanding of the applications of tensor products in different fields to better comprehend the purpose of the exercise.

Similar threads

  • Linear and Abstract Algebra
Replies
19
Views
2K
Replies
3
Views
2K
Replies
3
Views
1K
  • Linear and Abstract Algebra
Replies
6
Views
3K
  • Linear and Abstract Algebra
Replies
2
Views
1K
  • Advanced Physics Homework Help
Replies
5
Views
2K
  • Linear and Abstract Algebra
Replies
4
Views
882
  • Linear and Abstract Algebra
Replies
8
Views
2K
  • Linear and Abstract Algebra
Replies
4
Views
2K
  • Linear and Abstract Algebra
Replies
4
Views
2K
Back
Top