Understanding Vector Spaces: Axioms and Operations

In summary, the conversation discussed the 10 axioms of vector spaces and how they apply to the given objects u, v, and w. The approach of using ordered pairs to represent the objects was questioned, but ultimately deemed to be correct. It was determined that all axioms were satisfied except for axiom 5, but further analysis showed that axioms 4 and 5 both hold.
  • #1
dcramps
43
0

Homework Statement


aedE9.png

Homework Equations


The 10 axioms:
1. If u and v are objects in V, then u + v is in V
2. u + v = v + u
3. u + (v+w) = (u+v)+w
4. There is an object 0 in V, called a zero factor for V, such that 0+u = u+0 = u for all u in V
5. For each u in V, there is an object -u in V, called a negative of u, such that u+(-u)=(-u)+u = 0
6. If k is any scalar and u is any object in V, then ku is in V
7. k(u + v) = ku + kv
8. (k + m)u = ku + mu
9. k(mu) = (km)(u)
10. 1u = u

The Attempt at a Solution


First of all, are u, v, and w just (x_{1},y_{1}), (x_{2},y_{2}), (x_{3},y_{3}) and so on? If so, am I going about this the right way?

Axiom 1. Since x,y[tex]\in[/tex]R, then any (x,y)[tex]\in[/tex] thus the addition would hold. (Not really sure how to explain this one well)
Axiom 2. [tex](x_{1},y_{1})+(x_{2},y_{2}) = (x_{1}+x_{2}+_{1},y_{1}+y_{2}+_{1}) = (x_{2},y_{2})+(x_{1},y_{1}) = (x_{2}+x_{1}+_{1},y_{2}+y_{1}+_{1})[/tex]
Axiom 3.
[tex]u=(x_{1},y_{1})[/tex]
[tex]v=(x_{2},y_{2})[/tex]
[tex]w=(x_{3},y_{3})[/tex]

[tex]u+(v+w) = (x_{1},y_{1})+(x_{2}+x_{3}+1,y_{2}+y_{3}+1) = (x_{1}+(x_{2}+x_{3}+1)+1,y_{1}+(y_{2}+y_{3}+1)+1) = (x_{1}+x_{2}+x_{3}+_{2},y_{1}+y_{2}+y_{3}+2)[/tex]
[tex](u+v)+w = (x_{1}+x_{2}+1,y_{1}+y_{2}+1) + (x_{3},y_{3}) = ((x_{1}+x_{2}+_{1})+x_{3}+1,(y_{1}+y_{2}+1)+y_{3}+1) = (x_{1}+x_{2}+x_{3}+_{2},y_{1}+y_{2}+y_{3}+2)[/tex]and so on for the rest...

Is this the correct approach? It seems that it is to me, but I may be way off here.
 
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  • #2
dcramps said:

Homework Statement


aedE9.png

Homework Equations


The 10 axioms:
1. If u and v are objects in V, then u + v is in V
2. u + v = v + u
3. u + (v+w) = (u+v)+w
4. There is an object 0 in V, called a zero factor for V, such that 0+u = u+0 = u for all u in V
That would be zero vector, not factor.
dcramps said:
5. For each u in V, there is an object -u in V, called a negative of u, such that u+(-u)=(-u)+u = 0
6. If k is any scalar and u is any object in V, then ku is in V
7. k(u + v) = ku + kv
8. (k + m)u = ku + mu
9. k(mu) = (km)(u)
10. 1u = u

The Attempt at a Solution


First of all, are u, v, and w just (x_{1},y_{1}), (x_{2},y_{2}), (x_{3},y_{3}) and so on? If so, am I going about this the right way?
Yes, u, v, and w and just ordered pairs.
dcramps said:
Axiom 1. Since x,y[tex]\in[/tex]R, then any (x,y)[tex]\in[/tex] thus the addition would hold.
You left something out. ... then any (x, y) [itex]\in[/itex] V, so V is closed under addition.
dcramps said:
(Not really sure how to explain this one well)
Axiom 2. [tex](x_{1},y_{1})+(x_{2},y_{2}) = (x_{1}+x_{2}+_{1},y_{1}+y_{2}+_{1}) = (x_{2},y_{2})+(x_{1},y_{1}) = (x_{2}+x_{1}+_{1},y_{2}+y_{1}+_{1})[/tex]
Axiom 3.
[tex]u=(x_{1},y_{1})[/tex]
[tex]v=(x_{2},y_{2})[/tex]
[tex]w=(x_{3},y_{3})[/tex]

[tex]u+(v+w) = (x_{1},y_{1})+(x_{2}+x_{3}+1,y_{2}+y_{3}+1) = (x_{1}+(x_{2}+x_{3}+1)+1,y_{1}+(y_{2}+y_{3}+1)+1) = (x_{1}+x_{2}+x_{3}+_{2},y_{1}+y_{2}+y_{3}+2)[/tex]
[tex](u+v)+w = (x_{1}+x_{2}+1,y_{1}+y_{2}+1) + (x_{3},y_{3}) = ((x_{1}+x_{2}+_{1})+x_{3}+1,(y_{1}+y_{2}+1)+y_{3}+1) = (x_{1}+x_{2}+x_{3}+_{2},y_{1}+y_{2}+y_{3}+2)[/tex]


and so on for the rest...

Is this the correct approach? It seems that it is to me, but I may be way off here.
Yes, this is the right approach. I have my suspicions, though, that V is not a vector space.
 
  • #3
Thanks for the tip and clarification :]

I think you are correct, unless I made a mistake. Using this approach I found that all axioms were satisfied except axiom 5. Thoughts on this?
 
  • #4
Axiom 4 is also violated. There might be others, but I didn't check all of them.
 
  • #5
dcramps said:
Thanks for the tip and clarification :]

I think you are correct, unless I made a mistake. Using this approach I found that all axioms were satisfied except axiom 5. Thoughts on this?

Mark44 said:
Axiom 4 is also violated. There might be others, but I didn't check all of them.
Both axioms hold, actually. The zero vector would be 0=(-1,-1), and the additive inverse would be -(x,y) = (-x-2,-y-2). I would have thought one of axioms 7-10 would fail, but I think they all work out.
 
  • #6
Live and learn. Thanks, Vela.
 

1. What is a vector space?

A vector space is a mathematical concept that describes a set of objects, known as vectors, along with operations that can be performed on those vectors. These operations include addition and multiplication by a scalar (a number), and they must follow certain rules in order for the set to be considered a vector space.

2. How do you determine if a set is a vector space?

In order for a set of objects to be considered a vector space, it must satisfy a set of axioms, or rules. These include closure under vector addition and scalar multiplication, associativity of addition, existence of an additive identity element, and others. If a set satisfies all of these rules, it can be considered a vector space.

3. What is the dimension of a vector space?

The dimension of a vector space is the number of vectors that make up a basis for the space. A basis is a set of linearly independent vectors that span the entire space. In other words, any vector in the space can be written as a linear combination of the basis vectors. The dimension of a vector space is a fundamental property that helps to classify and understand different spaces.

4. Can a vector space have more than one basis?

Yes, a vector space can have multiple bases as long as they have the same number of vectors and are linearly independent. In some cases, different bases can be more convenient for different purposes. For example, in physics, different coordinate systems can be used as bases for vector spaces to describe the same physical phenomenon.

5. How is the dimension of a vector space related to its span?

The dimension of a vector space is equal to the number of vectors in its basis, and these vectors span the entire space. This means that any vector in the space can be written as a linear combination of the basis vectors. The span of a set of vectors is the set of all possible linear combinations of those vectors, and the dimension of the space is the minimum number of vectors needed to span the space.

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