Basis of a vector space - apparently simple problem

In summary: So the vectors satisfy the equation $3x -2y + 5z = 0$.I hope this helps you see where the vectors in the "null space" come from. They are just a parametric expression of the space of solutions to the equation $3x - 2y + 5z = 0$.In summary, the problem involves finding the basis of a subset of the vector space $\mathbb{R}^3$, which is defined by the equation 3x - 2y + 5z = 0. This can be done by finding the nullspace of the matrix (3 -2 5), which can be obtained by row-reducing the matrix to obtain a param
  • #1
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I am revising vector spaces and have got stuck on a problem that looks simple ... but ... no progress ...

Can anyone help me get started on the following problem ..

Determine the basis of the following subset of \(\displaystyle \mathbb{R}^3 \):

... the plane \(\displaystyle 3x - 2y + 5z = 0 \)

From memory (I studied vector spaces a long time ago!) I suspect that the solution would involve expressing the problem (but how?) in the form \(\displaystyle Av = b \) and then forming the matrix \(\displaystyle (A|b)\) and then reduce to reduced row echelon form ...

... but then, in this case, it seems that \(\displaystyle Av = b \) is as follows:

\(\displaystyle Av = (3 \ -2 \ \ 5) (x \ y \ z)^T = 0 \)

... ... so we are dealing with reducing the matrix \(\displaystyle (3 \ -2 \ \ 5 \ 0)\) ... ?

This does not seem right ... must have made a mistake in formulating the problem ...

Can someone please clarify this situation for me ...

Help would be appreciated

Peter
 
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  • #2
There is nothing wrong here, you are on the right track. Finding a basis of the plane amounts to finding a basis of the nullspace of the following matrix (since that nullspace is the plane by definition):
$$\left [ \begin{matrix} 3 &-2 &5 \end{matrix} \right ]$$
So you row-reduce it as usual (don't be put off by the fact that it only has one row - apply the usual rules):
$$\left [ \begin{matrix} 1 &\frac{-2}{3} &\frac{5}{3} \end{matrix} \right ]$$
The matrix has rank $1$, and so its nullspace has dimension $2$ (as expected, it's a plane) and so it has two free variables, and is of the form:
$$N = \left [ \begin{matrix} \frac{2}{3} y + \frac{-5}{3} z \\ y \\ z \end{matrix} \right ]$$
And so:
$$N = \left [ \begin{matrix} \frac{2}{3} y \\ y \\ 0 \end{matrix} \right ] + \left [ \begin{matrix} \frac{-5}{3} z \\ 0 \\ z \end{matrix} \right ]$$
Which can be rewritten as:
$$N = y \left [ \begin{matrix} \frac{2}{3} \\ 1 \\ 0 \end{matrix} \right ] + z \left [ \begin{matrix} \frac{-5}{3} \\ 0 \\ 1 \end{matrix} \right ]$$
And so we conclude that a basis of the nullspace of the matrix (and so, a basis of the plane) is:
$$\left \{ \left [ \begin{matrix} \frac{2}{3} \\ 1 \\ 0 \end{matrix} \right ], \left [ \begin{matrix} \frac{-5}{3} \\ 0 \\ 1 \end{matrix} \right ] \right \}$$
 
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  • #3
Bacterius said:
There is nothing wrong here, you are on the right track. Finding a basis of the plane amounts to finding a basis of the nullspace of the following matrix (since that nullspace is the plane by definition):
$$\left [ \begin{matrix} 3 &-2 &5 \end{matrix} \right ]$$
So you row-reduce it as usual (don't be put off by the fact that it only has one row - apply the usual rules):
$$\left [ \begin{matrix} 1 &\frac{-2}{3} &\frac{5}{3} \end{matrix} \right ]$$
The matrix has rank $1$, and so its nullspace has dimension $2$ (as expected, it's a plane) and so it has one free variable, and is of the form:
$$N = \left [ \begin{matrix} \frac{2}{3} y + \frac{-5}{3} z \\ y \\ z \end{matrix} \right ]$$
And so:
$$N = \left [ \begin{matrix} \frac{2}{3} y \\ y \\ 0 \end{matrix} \right ] + \left [ \begin{matrix} \frac{-5}{3} z \\ 0 \\ z \end{matrix} \right ]$$
Which can be rewritten as:
$$N = y \left [ \begin{matrix} \frac{2}{3} \\ 1 \\ 0 \end{matrix} \right ] + z \left [ \begin{matrix} \frac{-5}{3} \\ 0 \\ 1 \end{matrix} \right ]$$
And so we conclude that a basis of the nullspace of the matrix (and so, a basis of the plane) is:
$$\left \{ \left [ \begin{matrix} \frac{2}{3} \\ 1 \\ 0 \end{matrix} \right ], \left [ \begin{matrix} \frac{-5}{3} \\ 0 \\ 1 \end{matrix} \right ] \right \}$$

Thanks Bacterius ... really appreciate your help...

But ... can you explain how you arrived at the matrix N and how it enters the process?

Peter
 
  • #4
Peter said:
Thanks Bacterius ... really appreciate your help...

But ... can you explain how you arrived at the matrix N and how it enters the process?

Peter

N isn't a matrix, it's a vector. Actually it's the parametric form of the nullspace of the matrix $\left [ \begin{matrix} 3 &-2 &5 \end{matrix} \right ]$. You obtain it the usual way, by rewriting dependent variables in terms of free variables (here we have $1x + (-2/3)y + (5/3)z = 0$, and $x$ depends on $y$ and $z$, so $x = (2/3)y - (5/3)z$) and then separating the variables to obtain a basis for this nullspace. I thought I'd give it a name, $N$ seemed suitable :D
 
  • #5
In "high-school" one would solve this like so:

$3x - 2y + 5z = 0$

$3x - 2y = -5z$

We have 1 equation, 3 unknowns. Picking any two values for $x,y$ determines $z$, picking any less than two values, isn't any help:

Suppose $x$ (for example), was known to be the constant $a$. This gives:

$-2y = -5z - 3a$ or equivalently:

$2y = 5z + 3a$. Now $\frac{3}{2}a$ is just another constant, let's call it $b$ for now. Dividing by 2 gives:

$y = \frac{5}{2}z + b$.

This is a line in the $yz$-plane, and as its slope is not 0, it takes on ALL values for $y$.

We could do this for each variable in turn, the results aren't all that illuminating, but they do tell us we need to know at least TWO variables to find the third. There is nothing restricting our choice (hence the name "free variables" or "free parameters").

On the other hand, if we know $x,y$ then $z = \frac{2}{5}y - \frac{3}{5}x$.

In other words, all of our vectors $(x,y,z)$ that satisfy this are of the form:

$\left(x,y,\frac{2}{5}y - \frac{3}{5}x\right) = x\left(1,0,-\frac{3}{5}\right) + y\left(0,1,\frac{2}{5}\right)$.

If one wants to avoid fractions, one can put then in the form:

$(5x,5y,2y-3x) = x(5,0,-3) + y(0,5,2)$, which means our plane is spanned by the vectors (5,0,-3) and (0,5,2).

Now, YOU need to show that this IS a linearly independent set (that (5,0,-3) and (0,5,2) do not lie on the SAME line through the origin).

As a sanity check note that if $(x,y,z) = (5,0,-3)$ that:

$3x -2y + 5z = 15 - 0 + -15 = 0$, and similarly for the other vector.
 

Related to Basis of a vector space - apparently simple problem

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of objects called vectors, along with operations such as addition and scalar multiplication. These operations follow specific rules and allow for the manipulation and study of vectors in a systematic way.

2. What are the basic properties of a vector space?

The basic properties of a vector space include closure under addition and scalar multiplication, associativity and commutativity of addition, distributivity of scalar multiplication over addition, and the existence of a zero vector and additive inverse for each vector.

3. How is a basis of a vector space defined?

A basis of a vector space is a set of linearly independent vectors that span the entire space. This means that any vector in the space can be written as a linear combination of the basis vectors. The number of vectors in the basis is called the dimension of the vector space.

4. What is the significance of a basis in a vector space?

A basis is important because it allows us to represent any vector in the space using a unique set of coordinates. This makes it easier to perform calculations and understand the structure of the space. Additionally, the dimension of a vector space is equal to the number of vectors in its basis, which provides important information about the space.

5. Can a vector space have more than one basis?

Yes, a vector space can have multiple bases. This is because there can be different sets of linearly independent vectors that span the same space. However, all bases for a particular vector space will have the same number of vectors, which is equal to the dimension of the space.

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