Prove a set is not a vector space

In summary, if v \in A \setminus \ker B then there exists a w \in A \setminus \ker B such that at least one of v + w and v - w is not in A.
  • #1
kostas230
96
3

Homework Statement



Let [itex]b[/itex] be a symetric bilinear form on [itex]V[/itex] and [itex]A = \{ v\in V : b\left(v,v\right)=0\}[/itex]. Prove that [itex]A[/itex] is not a vector space, unless [itex]A = 0[/itex] or [itex]A = V[/itex].

2. The attempt at a solution

If we suppose that [itex]A[/itex] is a vector space then for every [itex]v,w\in A[/itex] we must have: [itex]b\left(v+w,v+w\right) = b\left(v,w\right)[/itex]. This try didn't go anywhere. I think I should construct a counter example, but I wouldn't know from where to start.
 
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  • #2
Assume that ##V = \mathbb R^2## and that
$$
b(v,w) =
v^T
\begin{pmatrix}
1 & 0 \\ 0 & 0
\end{pmatrix}
w.
$$
For ##v = \begin{pmatrix} x \\ y\end{pmatrix}##, we now have
$$
b(v,v) = x^2
$$
so ##A## is the set of vectors with ##x = 0##, i.e., vectors of the form
$$
v = \begin{pmatrix} 0 \\ y \end{pmatrix},
$$
which is basically the vector space ##\mathbb R##. Thus ##A \neq 0## and ##A \neq V## while still being a vector space.

Are you sure the problem formulation is correct?
 
  • #3
I think so. It's problem 12, page 53 from O' Niell's book "Semi-Riemannian Geometry with Applications to General Relativity".
 
  • #4
I do not have that book but "Semi-Riemannian" suggests that eventually there will be a (pseudo-Riemannian) metric involved. There were no additional constraints on the bilinear form apart from just being symmetric? For example, the light-cone extending from the origin of Minkowski space is obviously not a vector space as addition of vectors on the light-cone can end up being time-like or space-like.
 
  • #5
No. V is an finitely dimensional real vector space over the real numbers and b is just a bilinear form. It doesn't mention semi-Riemannian or even Minkowski spaces.

Maybe the author made a mistake...
 
  • #6
I ran across a thread on stackexchange which suggests that the problem should have said that ##A## is not a vector space unless ##A=0## or ##A=N##, where ##N## is the nullspace of ##b##.
 
  • #7
kostas230 said:
No. V is an finitely dimensional real vector space over the real numbers and b is just a bilinear form.

In that case, you can write [itex]b(v,w) = v^TBw[/itex] for some real symmetric matrix [itex]B[/itex].

It is then easy to show that [itex]\ker B \subset A[/itex]. But [itex]A \subset \ker B[/itex] doesn't necessarily hold.

Consider the case where [itex]V = \mathbb{R}^4[/itex] and [itex]B[/itex] is diagonal with eigenvalues 1, 1, -1 and 0 so that [tex]
v^TBw = v_1w_1 + v_2w_2 - v_3w_3.
[/tex] Now [itex]\ker B = \{(0,0,0,t) : t \in \mathbb{R} \}[/itex] and [itex]A = \{(x,y,z,t) \in \mathbb{R}^4 : z^2 = x^2 + y^2\}[/itex] which is the product of a double cone and the real line. It's not a subspace of [itex]\mathbb{R}^4[/itex].

If it happens that [itex]A = \ker B[/itex] then [itex]A[/itex] is indeed a subspace. That [itex]A = \ker B[/itex] need not be [itex]\{0\}[/itex] or [itex]V[/itex] is shown by Orodruin's example.
 
  • #8
pasmith said:
In that case, you can write [itex]b(v,w) = v^TBw[/itex] for some real symmetric matrix [itex]B[/itex].

And since [itex]B[/itex] is symmetric it is diagonalisable, so there exists a basis of eigenvectors. There is no reason not to use that basis, so you can take [tex]
b(v,w) = \sum_{i=1}^n \lambda_i v_i w_i
[/tex] where [itex]v = \sum v_i e_i[/itex], [itex]w = \sum w_i e_i[/itex] and [itex]e_i[/itex] is an eigenvector of [itex]B[/itex] with eigenvalue [itex]\lambda_i[/itex].

Your aim is to show that if [itex]v \in A \setminus \ker B[/itex] then there exists a [itex]w \in A \setminus \ker B[/itex] such that at least one of [itex]v + w[/itex] and [itex]v - w[/itex] is not in [itex]A[/itex].
 

1. What are the basic requirements for a set to be considered a vector space?

In order for a set to be considered a vector space, it must satisfy the following requirements:

  1. It must contain a zero vector, denoted as 0.
  2. It must have closed under vector addition, meaning that if u and v are vectors in the set, then u + v is also in the set.
  3. It must have closed under scalar multiplication, meaning that if u is a vector in the set and c is a scalar, then cu is also in the set.
  4. It must have commutative, associative, and distributive properties for vector addition and scalar multiplication.
  5. It must have a unique additive inverse for each vector in the set.

2. How can we prove that a set is not a vector space?

There are a few ways to prove that a set is not a vector space:

  1. Show that the set does not contain a zero vector.
  2. Show that the set is not closed under vector addition or scalar multiplication.
  3. Show that the set does not have commutative, associative, or distributive properties for vector addition and scalar multiplication.
  4. Show that the set does not have a unique additive inverse for each vector.

3. Can a set be partially a vector space?

No, a set cannot be partially a vector space. In order for a set to be considered a vector space, it must satisfy all of the requirements mentioned in the first question.

4. Can a set be both a vector space and a subspace of another vector space?

Yes, a set can be both a vector space and a subspace of another vector space. A subspace is a subset of a vector space that also satisfies the requirements for a vector space.

5. Is there a shortcut to prove that a set is not a vector space?

There is no shortcut to prove that a set is not a vector space. Each requirement must be checked and proven to be satisfied for a set to be considered a vector space. It is important to carefully analyze each property and provide a counterexample if it is not satisfied.

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