Tensor Rank vs Type: Explained

In summary, the metric tensor is a rank 2 tensor. The rank of a tensor field can change from point to point.
  • #1
ddesai
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0
Tensors can be of type (n, m), denoting n covariant and m contravariant indicies. Rank is a concept that comes from matrix rank and is basically the number of "simple" terms it takes to write out a tensor. Sometimes, however, I recall seeing or hearing things like "the metric tensor is a rank 2 tensor" and also "the metric is a covariant 2-tensor or type 2 tensor" I assume the two concepts, that of "type" and "rank" are unrelated, but I want another perspective.

Also, in GR mostly we deal with tensor fields as well as tensors. At different points the rank (as in matrix rank) may be different. Is this true?
 
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  • #2
When books say "the metric tensor is a rank 2 tensor" they really mean it's a tensor field of type (0,2). In this context rank doesn't mean the dimension of the image of a linear operator acting on a vector space.
 
  • #3
Rank = n+m. The tensors of a tensor field will always be of the same type. For example you wouldn't have field that was vector-valued at some points and scalar-valued at others.
 
  • #4
@jcsd. That's clear. But you use the term "Rank" in a different way than for example, this paper: http://www.its.caltech.edu/~matilde/WeitzMa10Abstract.pdf. If we assume that rank is defined as it is in this paper, then can you still say it doesn't change as you move from point to point?

@Newton. So then folks mix the terminology.
 
  • #5
ddesai said:
@jcsd. That's clear. But you use the term "Rank" in a different way than for example, this paper: http://www.its.caltech.edu/~matilde/WeitzMa10Abstract.pdf. If we assume that rank is defined as it is in this paper, then can you still say it doesn't change as you move from point to point?

@Newton. So then folks mix the terminology.

I completely missed the analogy with matrix rank. I suppose the metric must always be rank 4 for this meaning of rank as it has a non-zero determinant. I also would guess that the rank of a tensor field could change from point to point, for example in any tensor field that was zero at some point, but wasn't a zero tensor field.
 

1. What is the difference between Tensor Rank and Tensor Type?

Tensor Rank refers to the number of dimensions or axes in a tensor, while Tensor Type refers to the data type of the elements within the tensor. In other words, Tensor Rank describes the structure of a tensor, whereas Tensor Type describes the type of data it can hold.

2. How are Tensor Rank and Tensor Type related?

The Tensor Rank and Tensor Type are both properties of a tensor and are not directly related to each other. However, the Tensor Type may affect the Tensor Rank, as some data types may require more dimensions to represent the data accurately.

3. What are some common Tensor Types?

Some common Tensor Types include integers, floating point numbers, and strings. Other types may include boolean values, complex numbers, or even tensors themselves.

4. Can a Tensor have a different Rank and Type for each dimension?

Yes, a tensor can have a different Rank and Type for each dimension. For example, a tensor may have a Rank of 3, meaning it has 3 dimensions, but the data type may be different for each dimension. This is known as a heterogeneous tensor.

5. How does understanding Tensor Rank and Type impact data analysis and machine learning?

Understanding Tensor Rank and Type is crucial in data analysis and machine learning as tensors are the primary data structures used in these fields. Knowing the Rank and Type of a tensor allows for efficient manipulation and processing of data, which is essential in creating accurate and effective models.

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