Dimension of a Tensor vector space

In summary, the conversation discusses the dimension of a vector space V_n formed by a symmetric tensor S^{a_1 ...a_n} with indices ranging from 1 to 3. This can be determined by finding the number of non-isomorphic combinations of a tensor of type S^{ \underbrace{1...1}_{s} \underbrace{2...2}_{r} \underbrace{3...3}_{t}}, where r+s+t =n. This can also be thought of as finding the number of ways to split a list of n indices into sets of 1's, 2's, and 3's.
  • #1
sgd37
213
8

Homework Statement



Having a symmetric tensor [tex] S^{a_1 ...a_n} [/tex] forming a vector space [tex] V_n [/tex] with indices taking values from 1 to 3; what is the dimension of such a vector space?

Homework Equations


The Attempt at a Solution



essentially this reduces to picking a tensor of type [tex] S^{ \underbrace{1...1}_{s} \underbrace{2...2}_{r} \underbrace{3...3}_{t}}[/tex] with r+s+t =n and seeing how many non isomorphic combinations there are. I'm not that skilled at combinatorics unfortunately
 
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  • #2
So... if I'm understanding correctly, this is a slightly roundabout way to ask how many independent components the tensor [itex]S[/itex] has?

Think about it this way: you have a list of [itex]n[/itex] indices which you need to split into a set of 1's, a set of 2's and a set of 3's, in that order. How many places can you choose to put the split between the 1's and the 2's?

Then, given that choice, how many places can you choose to put the split between the 2's and the 3's?
 
  • #3
worked it out a different way, thanks all the same
 

Related to Dimension of a Tensor vector space

1. What is the dimension of a tensor vector space?

The dimension of a tensor vector space is the number of basis vectors needed to span the space. It is also equal to the number of indices needed to fully describe a tensor in that space.

2. How is the dimension of a tensor vector space related to the rank of a tensor?

The dimension of a tensor vector space is equal to the rank of the tensor. This means that the number of basis vectors needed to span the space is equal to the number of indices needed to fully describe the tensor.

3. Can the dimension of a tensor vector space be greater than the number of indices in a tensor?

Yes, the dimension of a tensor vector space can be greater than the number of indices in a tensor. This is because a tensor can have additional symmetry or structure that reduces the number of independent components needed to fully describe it.

4. How does the dimension of a tensor vector space affect the operations that can be performed on tensors?

The dimension of a tensor vector space determines the number of indices that can be contracted or summed over in tensor operations. In general, tensors with higher dimensions allow for more complex operations to be performed.

5. How is the dimension of a tensor vector space related to the dimension of the underlying vector space?

The dimension of a tensor vector space is equal to the product of the dimensions of the underlying vector space. For example, a tensor in a 3-dimensional vector space (3 indices) will have a dimension of 3^3 = 27 in a tensor vector space.

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