Linear Algebra: Vector space axioms

In summary, the conversation discusses a particular space, the set of all polynomials of degree greater than or equal to three, and zero, and evaluates whether it is a vector space or not. One of the fundamental axioms for a vector space, 1*x = x, does not hold true for this set, leading to a discussion about closure under vector addition and the lack of a null/zero vector in the space.
  • #1
preet
98
0

Homework Statement


One of the fundamental axioms that must hold true for a set of elements to be considered a vector space is as follows:
1*x = x

I was given a particular space: The set of all polynomials of degree greater than or equal to three, and zero, and asked to evaluate whether or not it was a vector space or not. The one that doesn't hold true is 1*x=x (according to the solutions), but I don't understand why. I can't find a situation where the above axiom holds true. Could anyone help me out?

Thanks
Preet
 
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  • #2
I'd check the solutions again. The problem is closure under vector addition.
 
  • #3
The set of vectors and the vector addition must form an abelian group. That is true for those polynomials. However, your polynomial space lacks the "null/zero vector"; since we know that the scalar zero times any vector from your set should give the zero vector. Since the zero vector is not an element of the space, it follows that the polynomials' space plus vector addition plus scalar multiplication do not yield a vector space.

Daniel.
 
  • #4
Daniel, the original post says that zero is an element of the set. The problem is indeed vector addition, since [itex]x^3 + (-x^3 + 1) = 1[/itex], for exampl.
 
  • #5
Yes, of course. :redface:

Daniel.
 

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors and operations defined on those vectors. It follows a set of axioms, or rules, that govern how the vectors can be added and multiplied.

2. What are the axioms of a vector space?

The axioms of a vector space include closure under addition and scalar multiplication, associativity and commutativity of addition, existence of an additive identity, existence of a multiplicative identity, and distributivity of scalar multiplication over addition.

3. How does linear independence relate to vector spaces?

Linear independence is a property of a set of vectors in a vector space. It means that no vector in the set can be written as a linear combination of the other vectors, and it is a necessary condition for a basis of a vector space.

4. What is a basis of a vector space?

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

5. Can non-numeric objects be vectors in a vector space?

Yes, non-numeric objects can be vectors in a vector space as long as they follow the axioms of a vector space. For example, functions and polynomials can be vectors in appropriate vector spaces.

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