Simple Problem concerning tensor products

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In summary, In Section 10.4 of Dummit and Foote, Example 3 deals with the tensor product of two integers modulo m and n. The statement shows that the gcd of m and n can be written as a linear combination of them, which leads to the conclusion that dx = 0. This is because the gcd of two numbers can always be written as a linear combination of them.
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Actually this problem really only concerns greatest common denominators.

In Section 10.4, Example 3 (see attachment) , Dummit and Foote where we are dealing with the tensor product \(\displaystyle \mathbb{Z} / m \mathbb{Z} \otimes \mathbb{Z} / n \mathbb{Z}\) we find the following statement: (NOTE: d is the gcd of integers m and n)

"Since \(\displaystyle m(1 \otimes 1) = m \otimes 1 = 0 \otimes 1 = 0 \)

and similarly

\(\displaystyle n(1 \otimes 1) = 1 \otimes n = 1 \otimes 0 = 0 \)

we have

\(\displaystyle d(1 \otimes 1) = 0 \) ... ... "

Basically we have

mx = 0 and nx = 0 and have to show dx = 0

It must be simple but I cannot see it!

Can someone please help?

Peter
 
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This comes from the fact that the gcd of two numbers can always be written as a linear combination of them. If $d = \text{gcd}(m,n)$ then there are integers $s,t$ such that $d=sm+tn.$ Then $dx = (sm+tn)x = s(mx) + t(nx) = 0.$
 

FAQ: Simple Problem concerning tensor products

What is a tensor product?

A tensor product is an algebraic operation that combines two vectors or matrices to create a new vector or matrix. It is denoted by the symbol ⊗ (a small circle with a cross inside) and is commonly used in linear algebra and multivariate calculus.

What are the properties of a tensor product?

The tensor product has several important properties, including bilinearity, associativity, and distributivity. It is also commutative, meaning the order of the vectors or matrices does not affect the result of the operation.

How is a tensor product different from a cross product?

A tensor product and a cross product are two different types of mathematical operations. While a tensor product combines two vectors or matrices, a cross product only applies to three-dimensional vectors and produces a new vector that is perpendicular to the original two.

What are some real-world applications of tensor products?

Tensor products have numerous applications in physics, engineering, and computer science. They are used in quantum mechanics to represent the state of a quantum system, in computer graphics to manipulate images and 3D models, and in machine learning to process and analyze multidimensional data.

How can I calculate a tensor product?

The formula for calculating a tensor product varies depending on the dimensions and types of the vectors or matrices involved. In general, it involves multiplying each element of one vector or matrix by every element of the other and arranging the results in a new vector or matrix. There are also online calculators and software programs available for calculating tensor products.

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