Affine independence in terms of linear independence

In summary, the affine/linear independence relationship is visualized in terms of vectors in the two higher dimensions. The similarity between the two representations is explained by the fact that the affine/linear independence of vectors in the first two dimensions is equivalent to the linear independence of vectors in the third dimension.
  • #1
Wiseguy
6
0
This question mostly pertains to how looking at affine independence entirely in terms of linear independence between different families of vectors. I understand there are quite a few questions already online pertaining to the affine/linear independence relationship, but I'm not quite able to find something that helps my particular problem, nor am I able to make the connection on my own.

I want to try and understand how the linear independence of a family of ##n## difference vectors from any arbitrary 'origin' vector, say ##(\overrightarrow{a_i a_0}, \ldots, \overrightarrow{a_i a_j}, \ldots \overrightarrow{a_i a_n})## where ##a_i\ and\ a_j \in \mathbb{R}^{n}## and ##j \neq i## for any arbitrary 'origin' ##i \in I##, implies the linear independence of the whole family of ##(n+1)## vectors ##(\hat{a_0}, \ldots, \hat{a_n})## where ##\hat{a_j} = (1, a_j)##

I am able to understand this from the perspective of using families of points, but I am unable to visualize how I would construct this only using families of vectors. I've tried looking at the vectors as position vectors, but I think that way of thinking would not necessarily be correct.
 
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  • #2
Hello and welcome to physicsforums.

I'm afraid your notation is quite unusual.

What does ##\overrightarrow{a_i a_0},## represent? Given that you've said ##a_i\in\mathbb{R}## that would suggest that ##\overrightarrow{a_i a_0}=a_0-a_i\in\mathbb{R}##, which is a scalar. You can think of that as a vector if you like, but ##\mathbb{R}## as vector space has only one dimension, so you can't have more than one linearly independent vector in it..

What does the right hand side of ##
\hat{a_j} = (1, a_j)
## represent?
 
  • #3
andrewkirk said:
Hello and welcome to physicsforums.

I'm afraid your notation is quite unusual.

What does ##\overrightarrow{a_i a_0},## represent? Given that you've said ##a_i\in\mathbb{R}## that would suggest that ##\overrightarrow{a_i a_0}=a_0-a_i\in\mathbb{R}##, which is a scalar. You can think of that as a vector if you like, but ##\mathbb{R}## as vector space has only one dimension, so you can't have more than one linearly independent vector in it..

What does the right hand side of ##
\hat{a_j} = (1, a_j)
## represent?

Thank you for your welcome.

I apologize. I meant to write ##\mathbb{R}^{n}##, not ##\mathbb{R}##. Yes, the notation ##\overrightarrow{a_i a_0},## is just used to represent ##(a_i - a_0) \in \mathbb{R}^{n}##. We can keep it in the latter form if it makes more sense.

And ## \hat{a_j} ## is just that. A vector ##\in \mathbb{R}^{n+1}## comprising of ##(1, a_j)##. I would like to know the intuition as to why the linear independence of these forms are equivalent.
 
  • #4
Is it a proof, or a visualization, that you are missing? If it's a visualization, why not take a small concrete example.
The easiest that still has vector structure is n=2. Take for instance a0=(1,1), a1=(2,2), a2=(1,2). Draw a picture of these in ##\mathbb{R}^2## and then another of what you get with the move into ##\mathbb{R}^3##.
 

1. What is affine independence?

Affine independence refers to a set of points in a vector space that are not collinear and cannot be expressed as a linear combination of other points in the set. This means that no point in the set can be written as a linear combination of the others, but they still follow a specific pattern or relationship.

2. What is the difference between affine independence and linear independence?

The main difference between affine independence and linear independence is the presence of a translation component. In linear independence, the points are only related by scaling and adding, while in affine independence, they are related by both scaling and translation.

3. How is affine independence related to linear dependence?

Affine independence is the opposite of linear dependence. If a set of points is affine independent, it means that they cannot be expressed as a linear combination of each other, and therefore, they are linearly independent.

4. Why is affine independence important in linear algebra?

Affine independence is important in linear algebra because it allows us to better understand the relationship between points in a vector space. It also helps us to determine if a set of points can be used as a basis for the space, which is a crucial concept in linear algebra.

5. How do you test for affine independence?

To test for affine independence, we can use the affine combination test. This involves taking a set of points and checking if any point can be written as a linear combination of the others. If not, the points are affine independent. Another way to test for affine independence is by calculating the determinant of the matrix formed by the points. If the determinant is non-zero, the points are affine independent.

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