Prove Not a Vector Space

In summary, the conversation is discussing the definition of a vector space V consisting of 2x2 matrices with normal addition and a unique multiplication operation # defined as β#A=β(A^T), where A^T is the transpose of A. The conversation explores the possibility of a multiplicative identity existing in this vector space and how it would need to satisfy the axiom 1#A=A for all matrices A, but it is ultimately concluded that there is no such scalar β that satisfies this condition for all matrices.
  • #1
NullSpace0
25
0

Homework Statement


Let V= set of 2x2 matrices with the normal addition, but where multiplication is defined as: β#A=β(A^T) where A^T is the transpose of A.


Homework Equations


The axiom about 1#A=A


The Attempt at a Solution


I think that because you can show that not ALL matrices satisfy A=A^T, you can't have a vector space since the multiplication by 1 doesn't hold up.

But then I'm wondering whether I'm assuming that the multiplicative identity should be the "normal" 1 (ie that 1 is just the scalar 1 in a normal R^n vector space).

How do you prove a multiplicative identity absolutely does NOT exist?
 
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  • #2
So are you trying to prove that V is a vector space if it obeys the normal matrix addition, but has a unique multiplication scalar multiplication defined as # which is sort of a mapping from A to At?

Your notation is a bit confusing to me.
 
  • #3
NullSpace0 said:

Homework Statement


Let V= set of 2x2 matrices with the normal addition, but where multiplication is defined as: β#A=β(A^T) where A^T is the transpose of A.


Homework Equations


The axiom about 1#A=A


The Attempt at a Solution


I think that because you can show that not ALL matrices satisfy A=A^T, you can't have a vector space since the multiplication by 1 doesn't hold up.

But then I'm wondering whether I'm assuming that the multiplicative identity should be the "normal" 1 (ie that 1 is just the scalar 1 in a normal R^n vector space).

How do you prove a multiplicative identity absolutely does NOT exist?

If there were a scalar β that was a multiplicative identity it would have to satisfy β(A^T)=A for all matrices A. Show there isn't.
 

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of objects called vectors, which can be added and multiplied by scalars (such as numbers), and follow certain properties such as closure, associativity, and distributivity.

2. How do you prove that something is not a vector space?

To prove that something is not a vector space, you need to show that it violates at least one of the properties of a vector space. This can be done by finding a counterexample or by showing that one of the properties is not satisfied.

3. What are the most common properties that are violated in "prove not a vector space" problems?

The most common properties that are violated in "prove not a vector space" problems are closure (where the addition or multiplication of two vectors results in a vector that is not in the set), associativity (where the order of operations affects the result), and distributivity (where multiplying a vector by a scalar does not distribute over addition).

4. Can you provide an example of something that is not a vector space?

Yes, the set of all polynomials of degree 3 or higher is not a vector space because it violates the closure property. For example, if we add two polynomials of degree 3 or higher, the result may have a degree lower than 3, which means it is not in the set.

5. How do "prove not a vector space" problems relate to real-world applications?

In real-world applications, vector spaces are often used to model physical quantities such as forces, velocities, and electric fields. By understanding the properties of a vector space, we can determine if a set of objects can be considered as vectors and if they follow the rules of vector addition and scalar multiplication. "Prove not a vector space" problems help us to critically analyze and understand these properties in different contexts.

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