Taking the Tensor Product of Vectors

In summary, taking the tensor product of two tensors is straightforward. However, in the book "General Relativity" by Wald, the author discusses taking tensor products on tensors and then in the next paragraph mentions constructing tensors by taking tensor products on vectors and dual vectors. This may seem confusing, but it makes sense because a vector space V is isomorphic to V**. Therefore, when taking the tensor product of vectors, it is actually taking the tensor product of linear functionals on V*, which is equivalent. However, there is a question about the author's use of notation when constructing simple tensors, as the order of vector and dual vector arguments should not matter, but it is a matter of convention. Ideally, the author should have specified taking
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JonnyG
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What is meant by taking the tensor product of vectors? Taking the tensor product of two tensors is straightforward, but I am currently reading a book where the author is talking about tensor product on tensors then in the next paragraph declares that tensors can then be constructed by taking tensor products on vectors and dual vectors. Taking tensor products on dual vectors makes sense to me, but what sense does it make to take tensor products of vectors?EDIT: Okay, I think it is because a vector space $V$ is isomorphic to $V^{**}$. So when I am taking the tensor product of vectors, I am really taking the tensor product of linear functionals on $V^*$, correct?

I also have another question now, which I may as well ask in this thread. The book I am reading is "General Relativity" by Wald. At first he defines a tensor of type [itex](k,l)[/itex] to be a multilinear map [itex]T: V^* \times \cdots \times V^* \times V \times \cdots \times V \rightarrow \mathbb{R}[/itex], but then he literally says this on the next page: "Thus, one way of constructing tensors is to take outer products of vectors and dual vectors. A tensor which can be expressed as such an outer product is called simple. If [itex]\{v_{\mu}\}[/itex] is a basis of [itex]V[/itex] and [itex]\{v^{\nu^*}\}[/itex] is its dual basis, it is easy to show that the [itex]n^{k+l}[/itex] simple tensors [itex]\{v_{\mu_1} \otimes \cdots \otimes v_{\mu_k} \otimes v^{{\nu_1}^*} \otimes \cdots \otimes v^{{\nu_k}^*} \}[/itex] yield a basis for [itex]\mathcal{T}(k,l)[/itex]."

My question is, when he first defined a tensor, he defined it as a multilinear map on [itex](V^*)^k \times V^l[/itex]. But then if you look at the quote above, in his basis for the simple tensors, he starts the tensor product with the vectors first and the dual vectors last. Is this not incorrect, because the tensor product does not, in general, commute? Shouldn't he have put the dual vectors first and the vectors last in that tensor product?
 
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It doesn't matter, and is purely a matter of convention, whether the dual vector arguments are placed before the vector arguments or after them, because a vector cannot be submitted in a dual-vector argument slot. If ##T:V^*\times V\to\mathbb R## then ##T(\vec v,\tilde b)## is undefined (meaningless) for ##\vec v\in V,\tilde p\in V^*##.

The orderings that do make a difference are the ordering within the collection of vector arguments and the ordering within the collection of dual vector arguments, because changing that ordering will not render an application of the tensor to arguments meaningless, but will change it.

Nevertheless, ideally Wald would have instead written 'one way of constructing tensors is to take outer products of dual vectors and vectors', in order to avoid creating confusion.

Your solution to your first question is correct, by the way!
 
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1. What is a tensor product of vectors?

A tensor product of vectors is a mathematical operation that combines two or more vectors to create a new vector. It is used in linear algebra, physics, and other fields of science to describe multidimensional quantities.

2. How is the tensor product of vectors calculated?

The tensor product of two vectors is calculated by multiplying each element of the first vector by each element of the second vector and arranging the results in a matrix. The resulting matrix is the tensor product of the two vectors.

3. What is the purpose of taking the tensor product of vectors?

The tensor product of vectors is used to describe more complex quantities that cannot be represented by a single vector. It allows for the representation of multidimensional data and operations in a concise and efficient manner.

4. Can the tensor product of vectors be applied to more than two vectors?

Yes, the tensor product can be applied to any number of vectors. The resulting tensor will have a higher dimensionality depending on the number of vectors used in the operation.

5. How is the tensor product of vectors used in real-world applications?

The tensor product of vectors is used in a variety of fields, including physics, engineering, and computer science. It is used to describe physical quantities, such as force and displacement, and is also used in machine learning and data analysis to represent and manipulate multidimensional data.

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