Is there a more efficient way to determine the basis of a subspace?

In summary, there is a conversation about determining if three given vectors are linearly independent and finding a basis for the subspace they span. One person uses the definition of independence while the other suggests using matrices and determinants. Both methods lead to the conclusion that the subspace is two-dimensional and can be spanned by two of the given vectors.
  • #1
mathmari
Gold Member
MHB
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Hey! :eek:

We are given the vectors $\vec{a}=\begin{pmatrix}4\\ 1 \\ 0\end{pmatrix}, \vec{b}=\begin{pmatrix}2\\ 0 \\ 1\end{pmatrix}, \vec{c}=\begin{pmatrix}0\\ -2 \\ 4\end{pmatrix}$.

I have shown by calculating the deteminant $|D|=0$ that these three vectors are linearly dependent.

I want to give a basis of the subspace of $\mathbb{R}^3$ that is spanned by these three vectors.

To find the basis of the subspace $\text{ span }\{\vec{a}, \vec{b}, \vec{c}\}$ we have to find a minimal subset $V$ of $\{\vec{a}, \vec{b}, \vec{c}\}$ with $\text{ span }V=\text{ span } \{\vec{a}, \vec{b}, \vec{c}\}$.

We have to use the Gauss algorithm to find the linearly independent subset, right?

$\begin{bmatrix}
\begin{matrix}
4 & 2 & 0\\
1 & 0 & -2\\
0 & 1 & 4
\end{matrix}\left|\begin{matrix}
0\\
0\\
0
\end{matrix}\right.\end{bmatrix}\begin{matrix}
\\
2.\text{row} \leftrightarrow 3.\text{row}\\

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

So, we get $4\lambda_1+\lambda_2=0, \lambda_2+4\lambda_3=0$, and so $\lambda_1=2\lambda_3, \lambda_2=-4\lambda_3$.

Therefore, $(\lambda_1, \lambda_2, \lambda_3)=\lambda_3 (2,-4,1), \lambda_3\in \mathbb{R}$.

For $\lambda_3=1$ we get the coefficients $(2,-4,1)$. That means that $2\vec{a}-4\vec{b}+\vec{c}=0 \Rightarrow \vec{c}=-2\vec{a}+4\vec{b}$.

Do we have to check if the vectors $\vec{a}, \vec{b}$ are linearly independent? Or can we just conclude from here that the basis that we are looking for is $\{\vec{a}, \vec{b}\}$ ? (Wondering)
 
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  • #2
Personally, I don't like to immediately go to matrices and determinants for a problem like this. To determine whether or not these three vectors are independent, I would use the definition of independent- suppose there exist 3 numbers, a, b, and c, such that \(\displaystyle a\begin{bmatrix}4 \\ 1 \\ 0 \end{bmatrix}+ b\begin{bmatrix} 2 \\ 0 \\ 1\end{bmatrix}+ c\begin{bmatrix}0 \\ -2\\ 4\end{bmatrix}= \begin{bmatrix}0 \\ 0 \\ 0 \end{bmatrix}\)- must a= b= c= 0?

That is the same as the three equations 4a+ 2b=0, a-2c= 0, and b+ 4c= 0. From the first equation b= -2a. From the second equation a= 2c so b= -2(2c)= -4c. But the third equation also give b= -4c. These vectors are dependent.

So the sub space they span is not three dimensional, it is either one or two dimensional. If it is one dimensional then any one vector of the given three will span it. If it is two dimensional, then any two will span it.

Try the first two vectors, \(\displaystyle \begin{bmatrix}4 \\ 1 \\ 0 \end{bmatrix}\) and \(\displaystyle \begin{bmatrix} 2 \\ 0 \\ 1\end{bmatrix}\). Clearly they are independent because one is not a multiple of the other. Therefore this subspace is two dimensional and these two vectors span it.
 
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  • #3
HallsofIvy said:
Personally, I don't like to immediately go to matrices and determinants for a problem like this. To determine whether or not these three vectors are independent, I would use the definition of independent- suppose there exist 3 numbers, a, b, and c, such that \(\displaystyle a\begin{bmatrix}4 \\ 1 \\ 0 \end{bmatrix}+ b\begin{bmatrix} 2 \\ 0 \\ 1\end{bmatrix}+ c\begin{bmatrix}0 \\ -2\\ 4\end{bmatrix}= \begin{bmatrix}0 \\ 0 \\ 0 \end{bmatrix}\)- must a= b= c= 0?

That is the same as the three equations 4a+ 2b=0, a-2c= 0, and b+ 4c= 0. From the first equation b= -2a. From the second equation a= 2c so b= -2(2c)= -4c. But the third equation also give b= -4c. These equations are dependent.

So the sub space they span is not three dimensional, it is either one or two dimensional. If it is one dimensional then any one vector of the given three will span it. If it is two dimensional, then any two will span it.

Try the first two vectors, \(\displaystyle \begin{bmatrix}4 \\ 1 \\ 0 \end{bmatrix}\) and \(\displaystyle \begin{bmatrix} 2 \\ 0 \\ 1\end{bmatrix}\). Clearly they are independent because one is not a multiple of the other. Therefore this subspace is two dimensional and these two vectors span it.

I understand!

But my way would also be correct, or not? (Wondering)
 

FAQ: Is there a more efficient way to determine the basis of a subspace?

What is a subspace?

A subspace is a subset of a vector space that satisfies the properties of a vector space. In other words, it is a set of vectors that can be added together and multiplied by scalars to produce another vector within the same set.

How is a subspace different from a vector space?

A subspace is a subset of a vector space, while a vector space is a set of vectors that satisfy the properties of vector addition and scalar multiplication. A vector space can contain any combination of vectors, while a subspace is limited to a set of vectors that can be formed by linear combinations of a smaller set of vectors.

What is the basis of a subspace?

The basis of a subspace is a set of linearly independent vectors that span the entire subspace. This means that any vector within the subspace can be written as a unique combination of the basis vectors.

How do you find the basis of a subspace?

To find the basis of a subspace, you can use the method of Gaussian elimination to reduce the vectors within the subspace to their simplest form. The remaining linearly independent vectors will form the basis of the subspace.

Why is the basis of a subspace important?

The basis of a subspace is important because it allows us to represent all vectors within the subspace using a smaller set of vectors. This can simplify calculations and make it easier to understand the properties of the subspace.

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