Exploring Linear Dependence in $\mathbb{Z}_2^3$

In summary, we discussed a set of four vectors in a subspace of \mathbb{Z}_{2}^{3} and determined that the zero vector does not factor into the dimension of the subspace. After considering the linear dependence of the other three vectors, we found that they are linearly dependent and that the dimension of the subspace is 2. We also noted that the field \mathbb{Z}_{2} has only two elements, 1 and 0, and that adding vectors 2 and 3 gives 4, resulting in a dimension of 2.
  • #1
latentcorpse
1,444
0
HI. Okay
Consider
[itex]
\[\left(\begin{array}{c}
0\\
0\\
0\end{array}\right)\] ,

\[\left(\begin{array}{c}
1\\
1\\
0\end{array}\right)\] ,

\[\left(\begin{array}{c}
1\\
0\\
1\end{array}\right)\] ,

\[\left(\begin{array}{c}
0\\
1\\
1\end{array}\right)\] as a subspace of \mathbb{Z}_{2}^{3}

[/itex]
In my notes I've written that this is a 2 dimensional subspace. How?
As far as I can see they are all linealry dependent vectors as if you add 1 of each of them you get back to the zero vector. No?
 
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  • #2
Firstly note that the zero vector (the first one) doesn't factor into the dimension of the subspace. Just look at the other 3 vectors. Note that any set of vectors containing the zero vector is necessarily linearly dependent.

What happens if you add the 2nd vector to the 3rd one? After considering this, ask yourself if it's possible to express each of the remaining (non-zero) vectors in terms of the others.

And what do you mean by "adding of each of them"? Doing so only shows that the 4 vectors are linearly dependent, but doesn't help much in showing that dim=2.
 
  • #3
A set of vectors is linearly independent if and only if the only sum [itex]a_1v_1+ a_2v_2+ \cdot\cdot\cdot+ \a_nv_n= 0[/itex] is if [itex]a_1= a_2= \cdot\cdot\cdot= a_n= 0[/itex]. Obviously you can immediately drop the 0 vector, but you need to look at [itex]a_1(1, 1, 0)+ a_2(1, 0, 1)+ a_3(0, 1, 1)= (a_1+ a_2, a_1+ a_3, a_2+ a_3)= (0, 0, 0)[/itex].
In other words, [itex]a_1+ a_2= 0[/itex], [itex]a_1+ a_3= 0[/itex], [itex]a_2+ a_3= 0[/itex] has the obvious (trivial) solution [itex]a_1= a_2= a_3= 0[/itex]. Does it have any other, non-trivial, solutions?
 
  • #4
yes [itex] a_1 = a_2 = a_3 = 1 [/itex] is also a solution giving linear independence. So if the o vector doesn't contribut to dimension then as the other 3 vectors are linearly dependent, their span is a 1-dimensional subspace as opposed to a 2-dimensional one?
 
  • #5
latentcorpse said:
yes [itex] a_1 = a_2 = a_3 = 1 [/itex] is also a solution giving linear independence. So if the o vector doesn't contribut to dimension then as the other 3 vectors are linearly dependent, their span is a 1-dimensional subspace as opposed to a 2-dimensional one?

As Halls already elaborated in detail, what you ought to do is look if the given vectors are linearly dependent or independent by considering the dependence relation, which Halls perfectly well stated. Now if W is the subspace that is spanned by the given vectors, then in order to be able to find dim(W) we shall eliminate all dependent vectors in the set. That is we shall take only those that are linearly independent. And as far as i can see, the three last vectors, are all lin. independent, which would make dim(W)=3 insdead of 2.
 
  • #6
sutupidmath said:
As Halls already elaborated in detail, what you ought to do is look if the given vectors are linearly dependent or independent by considering the dependence relation, which Halls perfectly well stated. Now if W is the subspace that is spanned by the given vectors, then in order to be able to find dim(W) we shall eliminate all dependent vectors in the set. That is we shall take only those that are linearly independent. And as far as i can see, the three last vectors, are all lin. independent, which would make dim(W)=3 insdead of 2.
Remember that the field has only 2 elements 1 and 0, so that means that 1 is its own additive inverse. So adding vectors 2 and 3 gives 4 so dim=2.
 
  • #7
Defennder said:
Remember that the field has only 2 elements 1 and 0,

Hmm, i didn't see this. Well, yeah, then like you said, dim(W) would be 2.
 

1. What is linear dependence in $\mathbb{Z}_2^3$?

Linear dependence in $\mathbb{Z}_2^3$ refers to a set of vectors in the field $\mathbb{Z}_2$ with three components that can be combined to form the zero vector. In other words, one vector in the set can be expressed as a linear combination of the other two.

2. How is linear dependence determined in $\mathbb{Z}_2^3$?

To determine linear dependence in $\mathbb{Z}_2^3$, we can use the Gaussian elimination method. This involves creating a matrix with the vectors as its rows and then reducing it to row-echelon form. If there is a row of all zeros in the reduced matrix, then the vectors are linearly dependent.

3. What is the significance of exploring linear dependence in $\mathbb{Z}_2^3$?

Exploring linear dependence in $\mathbb{Z}_2^3$ allows us to understand the relationships between vectors in this field. It is also important in applications such as coding theory and cryptography, where linear dependence can affect the security and efficiency of algorithms.

4. Can linearly dependent vectors in $\mathbb{Z}_2^3$ be used for calculations?

Yes, linearly dependent vectors in $\mathbb{Z}_2^3$ can still be used for calculations. However, they may not provide unique solutions and may result in errors or inconsistencies in the calculations.

5. How does linear dependence in $\mathbb{Z}_2^3$ differ from linear dependence in other fields?

The concept of linear dependence is the same in all fields, but the specific calculations and methods used to determine it may differ. In $\mathbb{Z}_2^3$, the only possible values are 0 and 1, which can lead to unique properties and differences in comparison to other fields.

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