On Sun, Nov 09, 2025 at 01:11:47AM -0800, Alan Grayson wrote:
> 
> 
> On Saturday, November 8, 2025 at 6:25:17 AM UTC-7 Alan Grayson wrote:
> 
>     In some treatments of tensors, they're described as linear maps. So, in 
> GR,
>     if we have a linear map described as a 4x4 matrix of real numbers, which
>     operates on a 4-vector described as a column matrix with entries (ct, x, 
> y,
>     z), which transforms to another 4-vector, what must be added in this
>     description to claim that the linear transformation satisfies the
>     definition of a tensor? TY, AG
> 
> 
> Let's call the linear transformation T, then the answer to my question might 
> be
> that T is a tensor iff it has a continuous inverse.  I'm not sure if this is
> correct, but I seem to recall this claim in a video about tensors I viewed in
> another life. But even if it's true, it seems to conflict with the claim that
> an ordinary vector in Euclidean space is a tensor because it's invariant under
> linear (?) transformations. In this formulation, it is the argument of T, 
> which
> we can call V, which is invariant, not the map T. I'd appreciate it if someone
> here could clarity my confusion. TY, AG

It's got nothing to do with being invertible (which is the conjunction
of being 1:1 and onto).

Rather a tensor is a multilinear map - is a map with multiple
arguments, and linear in each. Obviously a standard linear map R^n -> R^n is a
rank 2 tensor. We recognise them generally as matrices. Vectors
correspond to linear maps by means of transposing them and forming the
inner product, ie a linear map from R^n->R, and are rank 1 tensors as a result.



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