Graduate Need some clarifications on tensor calculus please

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The discussion revolves around clarifications needed in tensor calculus, specifically regarding tensor differentiation and inequalities involving positive semidefinite tensors. The user inquires about a clean notation for differentiating tensors of order greater than two, seeking a general expression for the gradient of the quadratic form involving a tensor. Additionally, they question whether an analogous inequality exists for tensors of higher orders, similar to the established one for order two tensors. Participants provide insights on tensor differentiation using Einstein notation and discuss the implications of symmetry in tensors. Overall, the conversation highlights the complexities of tensor calculus and the need for clear notation and understanding in higher-order cases.
pitaly
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Need some clarifications on tensor calculus
I've started reading up on tensors. Since this lies well outside my usual area, I need some clarifications on some tensor calculus issues.

Let ##A## be a tensor of order ##j > 1##. Suppose that the tensor is cubical, i.e., every mode is of the same size. So for example, if ##A## is of order 3 then ##A \in R^{n \times n \times n}##.

Further assume that ##A## is symmetric (sometimes called supersymmetric), i.e., it is invariant under permutation of indices. For example, if the order is 3 then ##A_{ijk} = A_{ikj} = A_{jik} = A_{jki} = A_{kij} = A_{kij}##.

We say that a tensor is positive semidefinite if ##x \cdot A \cdot x \geq 0## for all vectors ##x \neq 0##. So for example, if ##A## is of order 2, we have a standard quadratic form and if ##A## is positive semidefinite then ##x \cdot A \cdot x \geq 0##.

Now, to my two questions:

1) Is there any clean notation for tensor differentiation? If ##A## is of order 2 then the gradient of ##x \cdot A \cdot x## with respect to ##x## is ##2 x \cdot A##. How do I write the gradient of ##x \cdot A \cdot x## with respect to ##x## when ##A## is a tensor of any order ##j>2##?

2) Consider a tensor ##A## of order 2 and the quadratic form ##(x-y) \cdot A \cdot (x-y)## for any vectors ##x,y##. If ##A## is positive semidefinite then ##x \cdot A \cdot x - y \cdot A \cdot y \geq 2y \cdot A \cdot (x-y) ##. Does there exist any analogous inequality for tensors of any order ##j>2##, where the tensor ##A## is positive semidefinite?

Any help on this is highly appreciated!
 
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pitaly said:
Summary:: Need some clarifications on tensor calculus

I've started reading up on tensors. Since this lies well outside my usual area, I need some clarifications on some tensor calculus issues.

Let ##A## be a tensor of order ##j > 1##. Suppose that the tensor is cubical, i.e., every mode is of the same size. So for example, if ##A## is of order 3 then ##A \in R^{n \times n \times n}##.

Further assume that ##A## is symmetric (sometimes called supersymmetric), i.e., it is invariant under permutation of indices. For example, if the order is 3 then ##A_{ijk} = A_{ikj} = A_{jik} = A_{jki} = A_{kij} = A_{kij}##.

We say that a tensor is positive semidefinite if ##x \cdot A \cdot x \geq 0## for all vectors ##x \neq 0##. So for example, if ##A## is of order 2, we have a standard quadratic form and if ##A## is positive semidefinite then ##x \cdot A \cdot x \geq 0##.
What is ##x \cdot u \otimes v \otimes w \cdot x\;##?
pitaly said:
Now, to my two questions:

1) Is there any clean notation for tensor differentiation? If ##A## is of order 2 then the gradient of ##x \cdot A \cdot x## with respect to ##x## is ##2 x \cdot A##. How do I write the gradient of ##x \cdot A \cdot x## with respect to ##x## when ##A## is a tensor of any order ##j>2##?
The first that comes to my mind is the boundary operator in cohomology theory.
pitaly said:
2) Consider a tensor ##A## of order 2 and the quadratic form ##(x-y) \cdot A \cdot (x-y)## for any vectors ##x,y##. If ##A## is positive semidefinite then ##x \cdot A \cdot x - y \cdot A \cdot y \geq 2y \cdot A \cdot (x-y) ##. Does there exist any analogous inequality for tensors of any order ##j>2##, where the tensor ##A## is positive semidefinite?

Any help on this is highly appreciated!
Good question. I had the same (see above).

Let's see what you have actually done. You fed a tensor with two vectors and computed a scalar. Hence we need ##u^*,v^*\in V^*## to get ##(u^*\otimes v^*)(x,y)= u^*(x)v^*(y)\in \mathbb{R}.## We certainly can fill up this equation with arbitrary many ##w^{(k)}\in V## to get
$$
(u^*\otimes v^*\otimes w^{(1)}\otimes \ldots\otimes w^{(m)})(x,y)= u^*(x)v^*(y) \cdot w^{(1)}\otimes \ldots\otimes w^{(m)}
$$
but then we lose the possibility to compare it with the scalar ##0.##

I think you are approaching this from the wrong side. What do you want to achieve is the question you should ask, not how can I generalize something.

Since you asked in a mathematics forum, you may want to have a read:
https://www.physicsforums.com/insights/what-is-a-tensor/
https://www.physicsforums.com/insights/pantheon-derivatives-part-iii/
https://www.physicsforums.com/insights/pantheon-derivatives-part-iv/
 
pitaly said:
Summary:: Need some clarifications on tensor calculus

1) Is there any clean notation for tensor differentiation? If A is of order 2 then the gradient of x⋅A⋅x with respect to x is 2x⋅A. How do I write the gradient of x⋅A⋅x with respect to x when A is a tensor of any order j>2?
Not an expert, but I know my way around some basic tensor math. Normally I would the tensor product in Einstein notation, ie ##A_{ijk}x^ix^j##. Then I would write the derivative as a tensor-like expression: ##\partial_i = \frac{\partial}{\partial x^i}##. And then we can apply it:
$$\partial_l A_{ijk}x^ix^j = A_{ijk}\partial_l(x^ix^j)$$ Knowing the Leibniz rule and that ##\frac{\partial x^i}{\partial x^j}=\delta^i_j##, we can expand out the derivative: $$ \begin{align} \partial_l A_{ijk}x^ix^j & = A_{ijk}((\partial_l x^i)x^j + x^i\partial_l(x^j)) \\ & = A_{ijk}(\delta_l^ix^j+x^i\delta_l^j) \\ & = A_{ljk}x^j+A_{ilk}x^i\end{align}$$ In the case that ##A## is symmetric, we can freely swap the first two indices to get the expression ##A_{jlk}x^j+A_{ilk}x^i##, which are really two sums with the same form, so we can change the index to be the same:
$$A_{ilk}x^i+A_{ilk}x^i=2A_{ilk}x^i$$ I guess it looks kinda hand-wavey, but it’s really just a compact way of writing sums along indices. Add as many indices beyond ##l## to get the same identity for order >3.
 
pitaly said:
Further assume that ##A## is symmetric (sometimes called supersymmetric), i.e., it is invariant under permutation of indices. For example, if the order is 3 then ##A_{ijk} = A_{ikj} = A_{jik} = A_{jki} = A_{kij} = A_{kij}##.
I've never seen the expression "supersymmetric" for this, and seems very confusing; supersymmetry is something completely different. But maybe it's common in engineering texts.
 
pitaly said:
Summary:: Need some clarifications on tensor calculus

For example, if the order is 3 then Aijk=Aikj=Ajik=Ajki=Akij=Akij.
I think the last A term should be k j i .
 
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