What Does the Definition of a Contravariant Vector Mean in Tensor Analysis?

  • Thread starter HasuChObe
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In summary: This is a good explanation.In summary, Schaum's outlines define contravariant vectors as vectors that grow in the opposite direction to the basis vectors. This is different from what is stated in the example.
  • #1
HasuChObe
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So I'm looking at Schaum's outlines for Tensors and the definition of a Contravariant vector is
[tex]
\bar{T}^i=T^r\frac{\partial\bar{x}^i}{\partial x^r}
[/tex]
Where [itex]\bar{x}^i[/itex] and [itex]x^r[/itex] denote components of 2 different coordinates (the superscript does not mean 'to the power of') and [itex]T^i[/itex] and [itex]T^r[/itex] are contravariant tensors of order 1 (aka, a vector).

Lets say you have some 2-D vector [itex]{\bf v}[/itex]. It can be described as
[tex]
{\bf v}=\bar{T}^1\hat{\bar{e}}_1+\bar{T}^2\hat{\bar{e}}_2=T^1\hat{e}_1+T^2 \hat{e}_2
[/tex]
The vector [itex]{\bf v}[/itex] is the same length, but the basis for each vector may be different. If the operation from [itex](\hat{e}_1,\hat{e}_2)\rightarrow(\hat{\bar{e}}_1,\hat{\bar{e}}_2)[/itex] performs elongation, then [itex](T^1,T^2)\rightarrow(\hat{T}^1,\hat{T}^2)[/itex] will shrink (and vice versa) to preserve the shape of [itex]{\bf v}[/itex]. In this case, [itex](T^1,T^2)[/itex] are said to be contravariant vectors because they grow contrary to the direction that the basis grows in. However, the definition I found in Schaum's outlines seem to say the opposite.

For example, if
[tex]
\bar{T}^i=2,\,T^r=1,\,\frac{\partial\bar{x}^i}{\partial x^r}=2
[/tex]
Does that not say that going from unbarred to barred coordinates, the vector components are growing and so is the coordinate system? I must be confusing myself.
 
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  • #2
The last equation is unjustified.
 
  • #3
Can you elaborate?
 
  • #4
Let's just look at a 1 dimensional example (your example), take ##\bar{x}^1=2x^1## So that ##\frac{\partial \bar{x}^1}{\partial x^1}=2##. Then your example says if ##T^1=1## then ##\bar{T}^1=2##. Let's just assume the space is flat so things are easy.

But what does ##\bar{x}^1=2x^1## mean? At the point where the old ##x^1=1## the new ##\bar{x}^1=2##. So, if you think about it a bit, you will realize that your coordinates have actually shrank since you need a larger number to describe the same distance. In other words, it would be like if you switched from meters to half-meter measurements. Where the old coordinate says 1 meter, your new coordinate says 2 half meters. Therefore, your old vector was 1 meter long, your new vector is 2 half meters long. The length of the vector has remained invariant. In this example, the two vectors both point to a distance 1 meter from the origin!
 
  • #5
Good explanation!
 

1. What is a tensor?

A tensor is a mathematical object that represents a physical quantity or a geometric entity in a multidimensional space. It is a generalization of scalars, vectors, and matrices, and can be thought of as a multi-dimensional array of numbers.

2. What is the difference between a tensor and a matrix?

While both tensors and matrices are multi-dimensional arrays, tensors have higher dimensionality. Tensors can have any number of dimensions, while matrices are limited to two dimensions. Additionally, tensors follow certain transformation rules under coordinate changes, whereas matrices do not.

3. How are tensors used in science?

Tensors are used in many areas of science, including physics, engineering, and computer science, to model and analyze complex systems. They are particularly useful in fields that deal with multiple dimensions and non-Euclidean spaces, such as relativity and quantum mechanics.

4. Can you give an example of a tensor in real life?

A common example of a tensor in real life is stress tensor in materials science. It is a mathematical representation of the forces and stresses acting on a material in multiple dimensions. Tensors are also used in image and signal processing for tasks such as image recognition and sound analysis.

5. How do I perform operations on tensors?

Operations on tensors are similar to those on matrices, but they involve more indices due to their higher dimensionality. Addition, subtraction, and scalar multiplication are straightforward, while more complex operations like tensor multiplication and contraction require a deeper understanding of tensor algebra.

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