Tensor Rank vs Type: Explained

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Discussion Overview

The discussion centers on the concepts of tensor rank and type, particularly in the context of general relativity (GR). Participants explore the definitions and implications of these terms, how they relate to tensor fields, and the potential for variation in rank across different points in a field.

Discussion Character

  • Technical explanation
  • Debate/contested

Main Points Raised

  • Some participants propose that tensors are classified by type (n, m) indicating covariant and contravariant indices, while rank relates to the number of "simple" terms needed to express a tensor.
  • Others argue that when referring to the metric tensor as a rank 2 tensor, it is actually a tensor field of type (0,2), suggesting a distinction in terminology.
  • One participant states that rank can be defined as the sum of covariant and contravariant indices (n+m), asserting that tensors in a field maintain the same type throughout.
  • A later reply questions the consistency of the term "rank" as used in different contexts, referencing a specific paper that may define rank differently.
  • Another participant reflects on the analogy with matrix rank, suggesting that the metric tensor must always be rank 4 if defined in terms of having a non-zero determinant, while also speculating that the rank of a tensor field could vary at different points.

Areas of Agreement / Disagreement

Participants express differing views on the definitions and implications of tensor rank and type, indicating that there is no consensus on these concepts. The discussion remains unresolved regarding the relationship between rank and type, and whether rank can change across a tensor field.

Contextual Notes

There are limitations in the definitions of rank and type as they are used in various contexts, and the discussion highlights the potential for confusion arising from different terminologies in the literature.

ddesai
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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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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.
 
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.
 
@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.
 
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.
 

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