MHB How to Extend These Vectors to a Basis in $\mathbb{R}^4$?

  • Thread starter Thread starter mathmari
  • Start date Start date
  • Tags Tags
    Basis Vectors
mathmari
Gold Member
MHB
Messages
4,984
Reaction score
7
Hey! :o

Let $t\in \mathbb{R}$ and the vectors $$v_1=\begin{pmatrix}
0\\
1\\
-1\\
1
\end{pmatrix}, v_2=\begin{pmatrix}
t\\
2\\
0\\
1
\end{pmatrix}, v_3=\begin{pmatrix}
2\\
2\\
2\\
0
\end{pmatrix}$$ in $\mathbb{R}^4$.

I want to determine a maximal linearly independent subset of $\{v_1, v_2, v_3\}$ and to extend these to a basis of $\mathbb{R}^4$.
I have done the following:
$$\begin{pmatrix}
0 & t & 2 \\
1 & 2 & 2 \\
-1 & 0 & 2 \\
1 & 1 & 0
\end{pmatrix}\rightarrow \begin{pmatrix}
1 & 2 & 2 \\
0 & t & 2 \\
-1 & 0 & 2 \\
1 & 1 & 0
\end{pmatrix} \rightarrow \begin{pmatrix}
1 & 2 & 2 \\
0 & t & 2 \\
0 & 1 & 2 \\
0 & 1 & 2
\end{pmatrix}\rightarrow \begin{pmatrix}
1 & 2 & 2 \\
0 & t & 2 \\
0 & 1 & 2 \\
0 & 0 & 0
\end{pmatrix}\rightarrow \begin{pmatrix}
1 & 2 & 2 \\
0 & t & 2 \\
0 & 0 & 2t-2 \\
0 & 0 & 0
\end{pmatrix}$$

If $t\neq 1$ we get the following system: $$\lambda_1+2\lambda_2+2\lambda_3=0 \\ t\lambda_2+2\lambda_3=0 \\ (2t-2)\lambda_3=0$$
So, $\lambda_1=\lambda_2=\lambda_3=0$ and so the vectors are linearly independent.

If $t=1$ we get the system: $$\lambda_1+2\lambda_2+2\lambda_3=0 \\ \lambda_2+2\lambda_3=0=0$$
So, $(\lambda_1, \lambda_2, \lambda_3)=\lambda_3 (2, -2, 1), \lambda_3\in \mathbb{R}$.
Therefore, $v_3=-2v_1+2v_2$ and $v_1$ and $v_2$ are linearly independent. Is everything correct so far? (Wondering)

So do we get a different maximal linearly independent subset for $t=1$ and a different for $t\neq 1$ ? (Wondering) So, do we have to take cases for $t$ to extend the vectors to a basis? (Wondering)
 
Physics news on Phys.org
Or, since we are looking for the maximal linearly independent subset of $\{v_1,v_2,v_3\}$, we take the case $t\neq 1$, because then the linearly independent subset is the whole set? (Wondering)
 
There is a typo at the first post...

The vectors are\begin{equation*}v_1=\begin{pmatrix}
0\\
1\\
-1\\
t
\end{pmatrix}, v_2=\begin{pmatrix}
t\\
2\\
0\\
1
\end{pmatrix}, v_3=\begin{pmatrix}
2\\
2\\
2\\
0
\end{pmatrix}\end{equation*} in $\mathbb{R}^4$.

We have the following:
\begin{align*}&\begin{pmatrix}
0 & t & 2 \\
1 & 2 & 2 \\
-1 & 0 & 2 \\
t & 1 & 0
\end{pmatrix}\rightarrow \begin{pmatrix}
1 & 2 & 2 \\
0 & t & 2 \\
0 & 2 & 4 \\
t & 1 & 0
\end{pmatrix} \rightarrow \begin{pmatrix}
1 & 2 & 2 \\
0 & t & 2 \\
0 & 1 & 2 \\
0 & 1-2t & -2t
\end{pmatrix}\rightarrow \begin{pmatrix}
1 & 2 & 2 \\
0 & t & 2 \\
0 & 1 & 2 \\
0 & 2t & 2+2t
\end{pmatrix}\rightarrow \begin{pmatrix}
1 & 2 & 2 \\
0 & 1 & 2 \\
0 & t & 2 \\
0 & t & 1+t
\end{pmatrix}\\ &\overset{ t\neq 1}{\rightarrow } \begin{pmatrix}
1 & 2 & 2 \\
0 & 1 & 2 \\
0 & t & 2 \\
0 & 0 & t-1
\end{pmatrix}\rightarrow \begin{pmatrix}
1 & 2 & 2 \\
0 & 1 & 2 \\
0 & 0 & t-1 \\
0 & 0 & 0
\end{pmatrix}\end{align*} So, for $t\neq 1$ the $3$ vectors are linearly independent.

For $t=1$ we get fpr example $v_3=-2v_1+2v_2$, where $v_1$ and $v_2$ are linearly independent.

About extending the basis I have done the following:
When $t\neq 1$ :
We need one more vector. We write the given vectors as lilnes of a matrix and we add also a zero line:
\begin{equation*}\begin{pmatrix}
0 &
1&
-1 &
t
\\ t& 2& 0& 1 \\ 2&
2&
2&
0\\ 0&0&0&0
\end{pmatrix}\end{equation*}We have to bring the matrix into a row-echelon form \begin{equation*}\begin{pmatrix}
2 &
2&
2 & 0
\\ 0& 1& -1&t \\ 0 &
0&
4-4t&
2t^2-4t+2\\ 0&0&0&0
\end{pmatrix}\end{equation*}At the diagonals we write the element $1$ :
\begin{equation*}\begin{pmatrix}
2 &
2&
2 & 0
\\ 0& 1& -1&t \\ 0 &
0&
4-4t&
2t^2-4t+2\\ 0&0&0&1
\end{pmatrix}\end{equation*}

So, the vectors \begin{equation*}\begin{pmatrix}
0 \\
1\\
-1 \\ t\end{pmatrix}, \begin{pmatrix}
t\\ 2\\ 0\\ 1 \end{pmatrix} , \begin{pmatrix} 2 \\
2\\
2\\
0\end{pmatrix}, \begin{pmatrix}0\\ 0\\ 0\\ 1
\end{pmatrix}\end{equation*} form a basis.

Is this correct? (Wondering)
 
Hey mathmari! (Smile)

Let's bring the matrix in column echelon form instead of row echelon form. (Thinking)

$$\begin{array}{}
\quad \times \quad \phantom{0} \quad -t \\
\begin{pmatrix}
1 & 0 & t \\
1 & 1 & 2 \\
1 & -1 & 0 \\
0 & 1 & 1
\end{pmatrix} \to
\end{array}

\begin{array}{}
\quad \phantom{0} \quad \times \quad t-2 \\
\begin{pmatrix}
1 & 0 & 0 \\
1 & 1 & 2-t \\
1 & -1 & -t \\
0 & 1 & 1
\end{pmatrix} \to
\end{array}

\begin{array}{}
\phantom{0} \\
\begin{pmatrix}
1 & 0 & 0 \\
1 & 1 & 0 \\
1 & -1 & -2(t-1) \\
0 & 1 & t-1
\end{pmatrix}
\end{array}
$$

The columns are now our independent vectors.
For $t=1$ the third column is the 0 vector, showing that we have a dependent set, and furthermore the first 2 columns form an independent basis.
For $t\ne 1$, the third column is an independent vector with non-zero entries in the 3rd and 4th rows.

That means we can complete the basis by adding a 4th column that completes the column echelon form:
$$
\to\left(\begin{array}{ccc|c}
1 & 0 & 0 & 0\\
1 & 1 & 0 & 0\\
1 & -1 & -2(t-1) & 0 \\
0 & 1 & t-1 & 1
\end{array}\right)
$$

So indeed, we can pick \begin{pmatrix}0\\0\\0\\1\end{pmatrix} as the 4th basis vector. (Happy)
 
Ah ok... Thank you very much! (Smile)
 
I asked online questions about Proposition 2.1.1: The answer I got is the following: I have some questions about the answer I got. When the person answering says: ##1.## Is the map ##\mathfrak{q}\mapsto \mathfrak{q} A _\mathfrak{p}## from ##A\setminus \mathfrak{p}\to A_\mathfrak{p}##? But I don't understand what the author meant for the rest of the sentence in mathematical notation: ##2.## In the next statement where the author says: How is ##A\to...
##\textbf{Exercise 10}:## I came across the following solution online: Questions: 1. When the author states in "that ring (not sure if he is referring to ##R## or ##R/\mathfrak{p}##, but I am guessing the later) ##x_n x_{n+1}=0## for all odd $n$ and ##x_{n+1}## is invertible, so that ##x_n=0##" 2. How does ##x_nx_{n+1}=0## implies that ##x_{n+1}## is invertible and ##x_n=0##. I mean if the quotient ring ##R/\mathfrak{p}## is an integral domain, and ##x_{n+1}## is invertible then...
The following are taken from the two sources, 1) from this online page and the book An Introduction to Module Theory by: Ibrahim Assem, Flavio U. Coelho. In the Abelian Categories chapter in the module theory text on page 157, right after presenting IV.2.21 Definition, the authors states "Image and coimage may or may not exist, but if they do, then they are unique up to isomorphism (because so are kernels and cokernels). Also in the reference url page above, the authors present two...

Similar threads

Back
Top