Vector Space Axioms which fail certain matrices

In summary, the conversation discusses the topic of Linear Algebra and a homework assignment involving identifying failing Vector Space Axioms for different types of vector spaces. The conversation mentions working through 10 axioms for a "regular" set and having difficulty with determining if three matrices fail any Vector Space Axioms. The conversation also asks for a step-by-step example of solving for a zero vector or zero matrix, and a discussion on how to check if a set has a zero vector. The expert suggests checking if the 0 matrix is in the set and clarifies that in order to be a vector space, a set must satisfy all axioms.
  • #1
gaborfk
53
0
Second week in Linear Algebra...

My homework involves of identifying all failing Vector Space Axioms for various sets of vector spaces. I did fine with a "regular" set like (x,y,z) which has an operation like k(x,y,z)=(kx,y,z). I have worked through all 10 of the axioms, comparing left sides with right sides to figure out which ones fail.

However, now I have to determine if three matrices fail any Vector Space Axioms. I have no clue on how to do matrices.

Here they are:

[tex]\left(\begin{array}{cc}a&1\\1&b\end{array}\right)[/tex]

[tex]\left(\begin{array}{cc}a&0\\0&b\end{array}\right)[/tex]

[tex]\left(\begin{array}{cc}a&a+b\\a+b&b\end{array}\right)[/tex]

I would appreciate any step by step example of another matrix for like axiom 5 which has to do with finding the zero vector, 0+u=u+0=u where 0 is not zero, but a zero vector or for this instance, a zero matrix. I am not looking for a solution, but a method of solving these vector spaces.

Thank you very much
 
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  • #2
If I remember correctly, if the following three hold true, then the rest will as well:

1. For each [itex]\vec{u},\vec{v}\in W[/itex], [itex]\vec{u}+\vec{v}\in W[/itex].

2. For all [itex]c\in\mathbb{R}[/itex] and [itex]\vec{u}\in W[/itex], [itex]c\vec{u}\in W[/itex].

3. The vector space contains the zero vector.
 
  • #3
No, consider for example W = R^2 with the usual addition operator, but we define c * (x, y) = (x^2, y). It fails distributivity.
 
  • #4
First, I have a problem with the wording. It's not the axioms that "fail", its the set of matrices! Yes, you could say "which axioms fail to be true for this set of matrices" but I would prefer "which axioms does this set of matices fail to satisfy".

In any case- do the obvious. Write down all of the axioms for a vectors space, try each of you generic matrices in each and see which are true and which aren't.

For example, one axiom is "closure" of addition. If you add two matrices of the first kind given:
[tex]\left(\begin{array}{cc}a&1\\1&b\end{array}\right) + \left(\begin{array}{cc}c&1\\1&d\end{array}\right)[/tex]
Is that still of the same form?
 
  • #5
No it is not. It is in the form of

[tex]\left(\begin{array}{cc}x&2\\2&y\end{array}\right)[/tex]

So that means it is open for addition and scalar multiplication...

However, how would I go about checking if there is a zero vector?

Thank you
 
  • #6
Well, what happens when you plug in x=0, y=0? The constants remain, so what does that tell you?
 
  • #7
If U is a subspace of vector space V, then U "inherits" its operations from V- adding an multiplying are the same in both so the additive and multiplicative identities ('0' and '1') must be the same. All you need to do to check if U has a additive identity is to check if the 0 matrix is in it.
By they way, in order to be a vector space, a set of vectors must satisfy ALL the axioms. Once you have show that the space is not closed under addition or multiplication (I would not say "open" under the operations!) it is not necessary to check the other axioms. Even if they were true, the set could not be a subspace.
 

1. What are the vector space axioms?

The vector space axioms are a set of mathematical rules that define the properties and operations of a vector space. These axioms include closure, commutativity, associativity, distributivity, and the existence of an identity element and inverse elements for addition and multiplication.

2. How do these axioms fail for certain matrices?

These axioms can fail for certain matrices when the matrices do not satisfy the properties of a vector space. For example, a matrix may not have an inverse for multiplication or may not have a zero element, which violates the vector space axioms.

3. Can you provide an example of a matrix that fails the vector space axioms?

One example of a matrix that fails the vector space axioms is the zero matrix, which does not have an inverse for multiplication and does not have a zero element. Another example is a diagonal matrix with one or more zero entries, which does not have an inverse for addition.

4. What are the implications of these axioms failing for matrices?

If the vector space axioms fail for certain matrices, it means that these matrices do not behave like traditional vectors and cannot be manipulated using the standard vector operations. This may limit their applications in certain mathematical and scientific contexts.

5. Is it possible to modify these axioms to accommodate certain matrices?

Yes, it is possible to modify the vector space axioms to accommodate certain matrices. This is often done in the context of linear algebra, where matrices are used to represent linear transformations. In this case, the modified axioms may include additional properties specific to matrices, such as the existence of a matrix inverse for multiplication.

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