Why is the set of all 2x2 singular matrices not a vector space?

In summary, the set of all 2x2 singular matrices is not a vector space because the sum of two singular matrices can result in a nonsingular matrix, which violates one of the necessary requirements for a vector space.
  • #1
xvtsx
15
0

Homework Statement


The set of all 2x2 singular matrices is not a vector space. why?
[tex]\begin{bmatrix} 1 & 0\\ 0&0 \end{bmatrix}+\begin{bmatrix} 0 & 1\\ 0& 1 \end{bmatrix}=\begin{bmatrix} 1 & 1\\ 0 & 1 \end{bmatrix}[/tex]

Homework Equations


Is it because the determinant in both are zero, but by performing addition you get a nonsingular matrix from a two singular matrices.


The Attempt at a Solution


det(0)+det(0)=0
c*det(0) = 0
 
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  • #2
[tex]\begin{pmatrix}1&0\\0&0\end{pmatrix}+\begin{pmatrix}0&0\\0&1\end{pmatrix}=\ldots[/tex]
 
  • #3
arkajad said:
[tex]\begin{pmatrix}1&0\\0&0\end{pmatrix}+\begin{pmatrix}0&0\\0&1\end{pmatrix}=\ldots[/tex]

Sorry, but can you explain what you meant? Thanks
 
  • #4
Can you add these two matrices? Are they both singular? Is their sum singular? Is the set of singular matrices a vector space?
 
  • #5
They are both singular and if you add them up the result would be a nonsingular matrix. Singular matrices don't have a inverse, so they aren't vector spaces.
 
  • #6
xvtsx said:
They are both singular and if you add them up the result would be a nonsingular matrix. Singular matrices don't have a inverse, so they aren't vector spaces.

The last sentence is not a good one. In fact it is very very bad (it would be a good exercise for you to find out why it is so bad). A good one is:

In a vector space, for any two vectors from this space, their sum should be again a vector in the same space.

The examples show that this is not the case with singular matrices: one can find examples of two singular matrices whose sum is not a singular matrix. Therefore the set of all singular matrices does not satisfy one of the necessary requirements to be a vector space. Therefore it is not a vector space.
 

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of objects called vectors, and a set of operations that can be performed on those vectors. These operations include addition, scalar multiplication, and satisfying certain axioms such as closure, associativity, and distributivity.

2. What are the necessary properties for a vector space?

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

  • Closure: The result of any operation on vectors in the set must also be in the set.
  • Associativity: The order in which operations are performed on vectors does not affect the result.
  • Identity: There must be a vector called the zero vector that, when added to another vector, yields the original vector.
  • Inverses: Every vector must have an additive inverse, meaning that when added to the original vector, it yields the zero vector.
  • Scalar Multiplication: Vectors can be multiplied by scalars (real numbers) and the result is still in the vector space.
  • Distributivity: Scalar multiplication must distribute over vector addition.

3. How is a vector space represented mathematically?

In mathematical notation, a vector space is typically represented as (V, F, +, •), where V is the set of vectors, F is the field of scalars, + is the vector addition operation, and • is the scalar multiplication operation. The symbols used for the operations may differ depending on the context, but they must still satisfy the necessary properties for a vector space.

4. What are some examples of vector spaces?

Some common examples of vector spaces include:

  • The set of all n-dimensional real vectors, denoted as ℝn.
  • The set of all n-dimensional complex vectors, denoted as ℂn.
  • The set of all polynomials of degree n or less, denoted as Pn.
  • The set of all continuous functions on a given interval, denoted as C([a,b]).

5. How is linear independence related to vector spaces?

Linear independence is a property of a set of vectors in a vector space. A set of vectors is considered linearly independent if none of the vectors can be written as a linear combination of the others. In other words, no vector is redundant and each vector adds a unique dimension to the vector space. This property is important in determining the basis (a set of vectors that can be used to represent all other vectors in the space) and dimension of a vector space.

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