Vector Space Axioms: 4 Rules to Redefine

In summary, without commutativity and associativity, it is not possible to define a vector space in the usual way.
  • #1
pjallen58
12
0
I am trying to shorten and generalize the the definition of a vector space to redefine it in such a way that only four axioms are required. The axioms must hold for all vectors u, v and w are in V and all scalars c and d.

I believe the four would be:

1. u + v is in V,
2. u + 0 = u
3. u + -u = 0
4. cu is in V

I believe 1 and 2 can be used to satisfy:

u + v = v + u
(u + v) + w = u + (v + w)

and 3 and 4 can be used to satisfy:

c(u + v) = cu + cv
(c + d)u = cu + du
c(du) = (cd)u
1u = u

Not sure if I am on the right track here so any suggestions or corrections would be appreciated. Thanks to all who reply.
 
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  • #2
Actually I think that all the axioms are necessary, and if you leave out for example the commutativity or associativity axiom you don't get what people would ordinarily call a vector space.
If you think that you would, you should prove for example that "u + v = v + u" indeed follow from the four axioms you have, as you claim, though I wouldn't see how that could be done. In fact, I don't even see how to prove something as simple as 0 + u = u without using at least associativity ((u + v) + w = u + (v + w)) and -(-u) = u.
 
  • #3
pjallen58 said:
I believe the four would be:

1. u + v is in V,
2. u + 0 = u
3. u + -u = 0
4. cu is in V

I believe 1 and 2 can be used to satisfy:

u + v = v + u
(u + v) + w = u + (v + w)

A model for axioms 1 and 2 would be:
[tex]V:=\mathbb{Z}[/tex]
[tex]u\mathbf{+}v:=u-v[/tex]
[tex]\mathbf{0}:=0[/tex]

where 1 holds by the closure of integers under subtraction and 2 holds by the additive identity of integers. But in this model [itex]u+v\neq v+u[/itex] for most u and v.
 
  • #4
Ah! If you define [itex]-v:=v[/itex] and [itex]cv:=v[/itex] in the above model, you can see that 1, 2, 3, and 4 hold but commutativity still fails in general, as does (u + v) + w = u + (v + w). (It doesn't matter here, but let c be drawn from the reals.) With an appropriate step function instead for scalar multiplication (say cv := 0 for c = 0 and v = 1 and cv := v otherwise) you can make the scalar distribution properties fail as well.
 
Last edited:

Related to Vector Space Axioms: 4 Rules to Redefine

What are the vector space axioms?

The vector space axioms are a set of rules that define the properties of a vector space, which is a mathematical structure used to model physical quantities that have both magnitude and direction.

What are the 4 rules of vector space axioms?

The 4 rules of vector space axioms are closure, commutativity, associativity, and distributivity. Closure states that the result of any vector operation must be a vector within the same vector space. Commutativity states that the order of vector operations does not affect the result. Associativity states that the grouping of vector operations does not affect the result. Distributivity states that scalar multiplication can be distributed over vector addition.

Why are vector space axioms important?

Vector space axioms are important because they provide a framework for understanding and working with vectors in a mathematical setting. They also allow for the development of more complex mathematical structures, such as vector spaces over different fields.

Can the vector space axioms be applied to any type of vector?

Yes, the vector space axioms can be applied to any type of vector as long as it satisfies the properties defined by the axioms. This includes both geometric vectors, which have magnitude and direction, and abstract vectors, which can represent any type of mathematical object.

How do the vector space axioms relate to real-world applications?

The vector space axioms have numerous real-world applications, particularly in physics and engineering. They are used to model and analyze physical quantities, such as forces and velocities, and to solve problems involving vector operations. They are also used in computer graphics and machine learning, where vectors are used to represent data and perform calculations.

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