Can the vectors be written as a linear combination?

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Discussion Overview

The discussion revolves around the linear dependence of three vectors and whether two additional vectors can be expressed as linear combinations of them. Participants explore the application of Gaussian elimination to analyze the relationships among the vectors, focusing on both theoretical and computational aspects.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant claims that the vectors $\overrightarrow{a_1}, \overrightarrow{a_2}, \overrightarrow{a_3}$ are linearly dependent and presents a Gaussian elimination process to analyze this.
  • Another participant agrees with the dependency of the vectors and states that $\overrightarrow{b}$ can be expressed as a linear combination in infinitely many ways, while $\overrightarrow{c}$ cannot be expressed as such.
  • Further contributions reiterate the dependency of the vectors and confirm the conclusions regarding $\overrightarrow{b}$ and $\overrightarrow{c}$, with specific reference to the row-reduced forms of the matrices.
  • One participant suggests that the infinitely many solutions for $\overrightarrow{b}$ can be described using parameters derived from the equations obtained during the elimination process.
  • Another participant recommends using column elimination instead of row elimination to find a minimum set of independent vectors, suggesting it may simplify the process of identifying dependencies.

Areas of Agreement / Disagreement

Participants generally agree on the linear dependence of the vectors and the conclusions regarding $\overrightarrow{b}$ and $\overrightarrow{c}$. However, there is a suggestion for an alternative method (column elimination) that introduces a differing approach to the problem.

Contextual Notes

There are unresolved aspects regarding the specific methods of elimination and the implications of using different approaches, which may affect the identification of independent vectors and dependencies.

mathmari
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Hey! :o

We have the vectors $\overrightarrow{a_1}=\begin{pmatrix}1 \\ 2 \\ 3\end{pmatrix}, \overrightarrow{a_2}=\begin{pmatrix}-1 \\0 \\ 2\end{pmatrix}, \overrightarrow{a_3}=\begin{pmatrix}7 \\ 8 \\ 6\end{pmatrix}$.

I have shown that these vectors are linearly dependent:
$\begin{bmatrix}
\begin{matrix}
1 & -1 & 7\\
2 & 0 & 8\\
3 & 2 & 6
\end{matrix}\left|\begin{matrix}
0\\
0\\
0
\end{matrix}\right.\end{bmatrix}\begin{matrix}
\\
2.\text{row}-2\cdot 1.\text{row}\\
3.\text{row}-3\cdot 1.\text{row}
\end{matrix} \longrightarrow \begin{bmatrix}
\begin{matrix}
1 & -1 & 7\\
0 & 2 & -6\\
0 & 5 & -15
\end{matrix}\left|\begin{matrix}
0\\
0\\
0
\end{matrix}\right.\end{bmatrix}\begin{matrix}
\\
\\
3.\text{row}-\frac{5}{2}\cdot 2.\text{row}
\end{matrix} \longrightarrow \begin{bmatrix}
\begin{matrix}
1 & -1 & 7\\
0 & 2 & -6\\
0 & 0 & 0
\end{matrix}\left|\begin{matrix}
0\\
0\\
0
\end{matrix}\right.\end{bmatrix}$

right? (Wondering)

Can we write the vectors $\overrightarrow{b}=\begin{pmatrix}-6 \\ -4 \\ 2\end{pmatrix}$ and $\overrightarrow{c}=\begin{pmatrix}0 \\ 2 \\ 1\end{pmatrix}$ as linear combinations of the vectors $\overrightarrow{a_1}, \overrightarrow{a_2}, \overrightarrow{a_3}$ ? (Wondering)

To check this I applied the Gauss elimination algorithm:

$\begin{bmatrix}
\begin{matrix}
1 & -1 & 7\\
2 & 0 & 8\\
3 & 2 & 6
\end{matrix}\left|\begin{matrix}
-6\\
-4\\
2
\end{matrix}\right.\end{bmatrix}\begin{matrix}
\\
2.\text{row}-2\cdot 1.\text{row}\\
3.\text{row}-3\cdot 1.\text{row}
\end{matrix} \longrightarrow \begin{bmatrix}
\begin{matrix}
1 & -1 & 7\\
0 & 2 & -6\\
0 & 5 & -15
\end{matrix}\left|\begin{matrix}
-6\\
8 \\
20
\end{matrix}\right.\end{bmatrix}\begin{matrix}
\\
\\
3.\text{row}-\frac{5}{2}\cdot 2.\text{row}
\end{matrix} \longrightarrow \begin{bmatrix}
\begin{matrix}
1 & -1 & 7\\
0 & 2 & -6\\
0 & 0 & 0
\end{matrix}\left|\begin{matrix}
-6\\
8\\
0
\end{matrix}\right.\end{bmatrix}$ $\begin{bmatrix}
\begin{matrix}
1 & -1 & 7\\
2 & 0 & 8\\
3 & 2 & 6
\end{matrix}\left|\begin{matrix}
0\\
2\\
1
\end{matrix}\right.\end{bmatrix}\begin{matrix}
\\
2.\text{row}-2\cdot 1.\text{row}\\
3.\text{row}-3\cdot 1.\text{row}
\end{matrix} \longrightarrow \begin{bmatrix}
\begin{matrix}
1 & -1 & 7\\
0 & 2 & -6\\
0 & 5 & -15
\end{matrix}\left|\begin{matrix}
0\\
2 \\
3
\end{matrix}\right.\end{bmatrix}\begin{matrix}
\\
\\
3.\text{row}-\frac{5}{2}\cdot 2.\text{row}
\end{matrix} \longrightarrow \begin{bmatrix}
\begin{matrix}
1 & -1 & 7\\
0 & 2 & -6\\
0 & 0 & 0
\end{matrix}\left|\begin{matrix}
0\\
2\\
-2
\end{matrix}\right.\end{bmatrix}$

So, in both cases the vectors $\overrightarrow{b}$ and $\overrightarrow{c}$ cannot be written as linear combinations of the vectors $\overrightarrow{a_1}, \overrightarrow{a_2}, \overrightarrow{a_3}$.

Is everything correct? Could I improve something? (Wondering)
I have to give all the possible solutions of the linear equations system.

For the vector $\overrightarrow{b}$:

we get $2\lambda_2-6\lambda_3=8$ and $\lambda_1-\lambda_2+7\lambda_3=-6$.

Solving at the first equation for $\lambda_2$ we get $\lambda_2=4+3\lambda_3$.

From the other equation we get $\lambda_1=\lambda_2-7\lambda_3-6 =4+3\lambda_3-7\lambda_3-6=-2-4\lambda_3$.

So, are all the possible solutions of the linear equations system: $(\lambda_1, \lambda_2, \lambda_3)=(-2-4\lambda_3, 4+3\lambda_3, \lambda_3)=\lambda_3(-4, 3, 1)+(-2, 4, 0)$ ? (Wondering)
 
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Yes, these vectors are dependent. In showing whether or not vector \vec{b} can be written as a linear combination, you arrived at a reduced matrix in which all 4 entries in the last row equal to 0. since 0x+ 0y+ 0z= 0 for any x, y, z, that shows that \vec{b} can be written as a linear combination, in infinitely many ways.

For \vec{c} you arrived at a reduce matrix with last row "0 0 0 | -2". Since 0x+ 0y+ 0z is not equal to -2 or any x, y, z, yes, \vec{c} cannot be written as a linear combination.
 
HallsofIvy said:
Yes, these vectors are dependent. In showing whether or not vector \vec{b} can be written as a linear combination, you arrived at a reduced matrix in which all 4 entries in the last row equal to 0. since 0x+ 0y+ 0z= 0 for any x, y, z, that shows that \vec{b} can be written as a linear combination, in infinitely many ways.

For \vec{c} you arrived at a reduce matrix with last row "0 0 0 | -2". Since 0x+ 0y+ 0z is not equal to -2 or any x, y, z, yes, \vec{c} cannot be written as a linear combination.

I understand! (Smile)

And are the infinitely many ways for $\vec{b}$ described as follows?

mathmari said:
For the vector $\overrightarrow{b}$:

we get $2\lambda_2-6\lambda_3=8$ and $\lambda_1-\lambda_2+7\lambda_3=-6$.

Solving at the first equation for $\lambda_2$ we get $\lambda_2=4+3\lambda_3$.

From the other equation we get $\lambda_1=\lambda_2-7\lambda_3-6 =4+3\lambda_3-7\lambda_3-6=-2-4\lambda_3$.

So, are all the possible solutions of the linear equations system: $(\lambda_1, \lambda_2, \lambda_3)=(-2-4\lambda_3, 4+3\lambda_3, \lambda_3)=\lambda_3(-4, 3, 1)+(-2, 4, 0)$ ? (Wondering)
 
Yes. Since your matrix can be row reduced a matrix with one row all 0s, this is equivalent two equations in three variables, a, b, and c. You can solve for two of the variables in terms of the third, or solve for all three in terms of a new parameter. Here you have the single parameter \lambda_3.
 
mathmari said:
Could I improve something? (Wondering)

Hey mathmari! (Smile)

I would recommend using column elimination instead of row elimination.
As a result we'll find the minimum set of independent vectors that also makes it easier to find the other dependencies. (Nerd)
 
Ah ok...

Thank you very much! (Happy)
 

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