Linearly Independent: Why ${}\left\{X+Y, Y+Z, Z+W, W+X\right\}$ Isn't

In summary, the set $\{X+Y, Y+Z, Z+W, W+X\}$ is dependent because it can be expressed as a linear combination of the vectors in the set $\{X, Y, Z, W\}$. The conditions given in the conversation do not contradict this fact.
  • #1
Dethrone
717
0
Let ${}\left\{X, Y, Z, W\right\}$ be an independent set in $\Bbb{R}^n$, is the following set independent?
${}\left\{X+Y, Y+Z, Z+W, W+X\right\}$

My textbook says it isn't, but I'm not sure why. Let $\lambda_1, \lambda_2, \lambda_3, \lambda_4$ be scalars, then $\lambda_1(X+Y)+\lambda_2(Y+Z)+\lambda_3(Z+W)+\lambda_4(W+X)=0$. Expanding an simplifying, we get $(\lambda_1+\lambda_4)X+(\lambda_1+\lambda_2)Y+(\lambda_2+\lambda_3)Z+(\lambda_3+\lambda_4)W=0$.

Since we are given that ${}\left\{X, Y, Z, W\right\}$ is independent, then $(\lambda_1+\lambda_4)=0$, $(\lambda_1+\lambda_2)=0$, $(\lambda_2+\lambda_3)=0$, and $(\lambda_3+\lambda_4)=0$. This implies that $\lambda_1=\lambda_3=-\lambda_2=-\lambda_4=0$. Why is it not independent, then?
 
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  • #2
Hello again Rido12,

The set $\{X + Y, Y + Z, Z + W, W + X\}$ is dependent because $$(X + Y) - (Y + Z) + (Z + W) - (W + X) = 0.$$ This relation has nothing to do with the independence of $\{X, Y, Z, W\}$; the above identity holds for all $X, Y, Z, W \in \Bbb R^n$.
 
  • #3
Hi Euge! That makes complete sense, but how come I wasn't able to distill that solution when wrote it as a linear combination? What is wrong with the reasoning?
Starting with this line:
$\lambda_1(X+Y)+\lambda_2(Y+Z)+\lambda_3(Z+W)+\lambda_4(W+X)=0$, a solution is clearly $\lambda_1=1, \lambda_2=-1, \lambda_3=1, \lambda_4=-1$, as you have pointed out.
What is wrong with rewriting that as $(\lambda_1+\lambda_4)X+(\lambda_1+\lambda_2)Y+(\lambda_2+\lambda_3)Z+(\lambda_3+\lambda_4)W=0$ and using ${}\left\{X, Y, Z, W\right\}$'s independence as a tool? Could the question be purposely trying to mislead, as that fact was clearly given as an assumption?
 
  • #4
You're making mistakes in your calculations. The conditions $\lambda_1 + \lambda_4 = \lambda_1 + \lambda_2 = \lambda_2 + \lambda_3 = \lambda_3 + \lambda_4$ imply $\lambda_1 = \lambda_3$ and $\lambda_2 = \lambda_4$. Since $\lambda_1 + \lambda_2 = 0$, $\lambda_1 = -\lambda_2$. The general solution will be $(\lambda_1, \lambda_2, \lambda_3, \lambda_4) = \lambda(1,-1,1,-1)$, $\lambda \in \Bbb R$.
 
  • #5
A set of vectors $\{\textbf{W}, \textbf{X}, \textbf{Y}, \textbf{Z}\}$ are $\textbf{Linearly Independent}$ if there exists scalars $a,b,c,d$ such that $a\textbf{W} + b\textbf{X} + c\textbf{Y} + d\textbf{Z} = \textbf{0}, a = b = c = d = \textbf{0}$.

A set of vectors $\{\textbf{W}, \textbf{X}, \textbf{Y}, \textbf{Z}\}$ are $\textbf{Linearly Dependent}$ if there exists scalars $a,b,c,d$ such that $a\textbf{W} + b\textbf{X} + c\textbf{Y} + d\textbf{Z} = \textbf{0}$ and $a - d$ are not all $\textbf{0}$.

Let $W = \begin{bmatrix} 1 \\ 0 \\ 0 \\ 0 \end{bmatrix}, X = \begin{bmatrix} 0 \\ 1 \\ 0 \\ 0 \end{bmatrix}, Y = \begin{bmatrix} 0 \\ 0 \\ 1 \\ 0 \end{bmatrix}, Z = \begin{bmatrix} 0 \\ 0 \\ 0 \\ 1 \end{bmatrix}$

Then

$X + Y = \begin{bmatrix} 0 \\ 1 \\ 1 \\ 0 \end{bmatrix}
\\Y + Z = \begin{bmatrix} 0 \\ 0 \\ 1 \\ 1 \end{bmatrix}
\\Z + W = \begin{bmatrix} 1 \\ 0 \\ 0 \\ 1 \end{bmatrix}
\\W + X = \begin{bmatrix} 1 \\ 1 \\ 0 \\ 0 \end{bmatrix}$

And
$\begin{bmatrix} 1 \\ 0 \\ 0 \\ 1 \end{bmatrix} + \begin{bmatrix} 0 \\ 1 \\ 1 \\ 0 \end{bmatrix} - \left(\begin{bmatrix} 0 \\ 0 \\ 1 \\ 1 \end{bmatrix} + \begin{bmatrix} 1 \\ 1 \\ 0 \\ 0 \end{bmatrix}\right)
= \begin{bmatrix} 1 \\ 1 \\ 1 \\ 1 \end{bmatrix} - \begin{bmatrix} 1 \\ 1 \\ 1 \\ 1 \end{bmatrix} = \textbf{0}$

Therefore:
$(Z + W) + (X + Y) - ((Y + Z) + (W + Z)) = \textbf{0}$
and $(a, b, c, d) = (1, 1, -1, -1)$

Conclusion:
The set $\{X + Y, Y + Z, W + Z, Z + W\}$ is not linearly independent.
 
  • #6
Thanks Euge and bwpbruce for the help!It turns out that I actually had the answer from the beginning when I stated $\lambda_1=\lambda_3=-\lambda_2=-\lambda_4$ in the first post, as it not only implies that $\lambda_1=\lambda_2=\lambda_3=\lambda_4=0$, but $\lambda_1=\lambda_3=1$ and $\lambda_2=\lambda_4=-1$ and vice-versa, which does satisfy the equation. Needless to say I probably stayed up too late that night.
 

Related to Linearly Independent: Why ${}\left\{X+Y, Y+Z, Z+W, W+X\right\}$ Isn't

1. What does it mean for a set of vectors to be linearly independent?

Linear independence refers to the property of a set of vectors in a vector space to not be able to be written as a linear combination of each other. In other words, no vector in the set can be expressed as a sum of multiples of the other vectors in the set.

2. Why is the set ${}\left\{X+Y, Y+Z, Z+W, W+X\right\}$ not linearly independent?

This set is not linearly independent because one of the vectors, for example X+Y, can be written as a linear combination of the other vectors in the set. X+Y = (Z+W) - (Y+Z) - W, which means it is not a unique combination of the other vectors.

3. Can a set of vectors be linearly independent in one vector space but not in another?

Yes, a set of vectors can be linearly independent in one vector space but not in another. This is because the vector space itself may have different dimensions or basis vectors, which can affect the linear independence of a set of vectors.

4. How do you determine if a set of vectors is linearly independent?

To determine if a set of vectors is linearly independent, you can use the method of Gaussian elimination. This involves creating a matrix with the vectors as columns, and reducing it to its reduced row echelon form. If the reduced matrix has a pivot in every column, the vectors are linearly independent.

5. Can a set of linearly dependent vectors be useful in any way?

Yes, a set of linearly dependent vectors can still be useful for certain applications. For example, in computer graphics, linearly dependent vectors can represent transformations or rotations in 3D space. However, for most mathematical and scientific purposes, linearly independent vectors are preferred.

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