Vector space properties: distributivity

V is a commutative group for addition and for the scalar multiplication we have:##1\overrightarrow a = \overrightarrow a####(\alpha \beta)\overrightarrow a = \alpha (\beta \overrightarrow a)####(\alpha + \beta)\overrightarrow a = \alpha \overrightarrow a + \beta \overrightarrow a####\alpha (\overrightarrow a + \overrightarrow b) = \alpha \overrightarrow a + \alpha \overrightarrow b##In summary, the vector-space axioms state that a vector space V is a commutative group for addition, and that scalar multiplication satisfies certain properties. These properties include the existence of a multiplicative identity, associativity, distribut
  • #1
member 587159

Homework Statement


I want to proof, using the axioms of a vector space, that:

##(\alpha - \beta)\overrightarrow a = \alpha \overrightarrow a - \beta \overrightarrow a##

Homework Equations



Definition vector space:

The Attempt at a Solution



##(\alpha - \beta)\overrightarrow a = (\alpha + (-\beta))\overrightarrow a = \alpha \overrightarrow a + (-\beta) \overrightarrow a##

I'm stuck here, I want to show that:

##(-\beta) \overrightarrow a = -(\beta \overrightarrow a)##

I showed that both have the same symmetric element. But I did not show that symmetric elements are unique. Is there an easier way as this is an exercise and I think I'm overseeing something.

EDIT: I cannot use ##(-1)\overrightarrow a = - \overrightarrow a##
 
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  • #2
Math_QED said:
I cannot use ##(-1)\overrightarrow a = - \overrightarrow a## \overrightarrow a##

Then prove it, so you can use it.
 
  • #3
micromass said:
Then prove it, so you can use it.

That's funny, because I already proved it but it uses this result.

Can I derive, from the definition of vector space that:

##\overrightarrow a + \overrightarrow b = \overrightarrow a+ \overrightarrow c \Rightarrow \overrightarrow b = \overrightarrow c##. If yes, I can show uniqueness of inverse elements and then it's proven. Is there an easier way?
 
  • #4
Math_QED said:
That's funny, because I already proved it but it uses this result.

Can I derive, from the definition of vector space that:

##\overrightarrow a + \overrightarrow b = \overrightarrow a+ \overrightarrow c \Rightarrow \overrightarrow b = \overrightarrow c##.

Yes, you should be able to derive this easily

If yes, I can show uniqueness of inverse elements and then it's proven. Is there an easier way?

This is the easiest way imo.
 
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  • #5
micromass said:
Yes, you should be able to derive this easily

I don't see how I can derive it that easily. Can you give me a hint?
 
  • #6
Math_QED said:
I don't see how I can derive it that easily. Can you give me a hint?

Add the additive inverse of ##a##.
 
  • #7
micromass said:
Add the additive inverse of ##a##.

Yeah, thought about that too. Why am I allowed to do that on both sides?
 
  • #8
Math_QED said:
Yeah, thought about that too. Why am I allowed to do that?

What's wrong with it?
 
  • #9
micromass said:
What's wrong with it?

I would use the property then, which I did not prove:

a + b = a + c => a + c + d = a + c + d
 
  • #10
Addition is a function.
 
  • #11
micromass said:
Addition is a function.

Yes, for vectors:

##+ : V \times V \rightarrow V## but how can I conclude that then?
 
  • #12
What does it mean by definition that ##+## is a function?
 
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  • #13
micromass said:
What does it mean by definition that ##+## is a function?

It means that:

##+ \subset ((V \times V) \times V)## such that for every 2 vectors ##(\overrightarrow x, \overrightarrow y) \in (V \times V)## there is a unique vector ##\overrightarrow z \in V## such that ##+(\overrightarrow x, \overrightarrow y) = \overrightarrow z##
 
  • #14
Math_QED said:
It means that:

##+ \subset ((V \times V) \times V)## such that for every 2 vectors ##(\overrightarrow x, \overrightarrow y) \in (V \times V)## there is a unique vector ##\overrightarrow z \in V## such that ##+(\overrightarrow x, \overrightarrow y) = \overrightarrow z##

And do you see that it means that if ##x=x'## and ##y = y'## then ##+(x,y) = +(x',y')##? To prove this, let ##z = +(x,y) = +(x',y')## and use uniqueness of ##z##.
 
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  • #15
micromass said:
And do you see that it means that if ##x=x'## and ##y = y'## then ##+(x,y) = +(x',y')##? To prove this, let ##z = +(x,y) = +(x',y')## and use uniqueness of ##z##.

Hm let me try:

Proof: Let ##(x,y) \in V \times V##

Then, there is a unique ##z \in V## such that ##z = +(x,y)##. But, since x = x' and y = y', we have that ##+(x',y') = z## too. Since z is unique, we conclude that ##+(x',y') = +(x,y)##
 
  • #16
Math_QED said:

Homework Statement


I want to proof, using the axioms of a vector space, that:

##(\alpha - \beta)\overrightarrow a = \alpha \overrightarrow a - \beta \overrightarrow a##

Homework Equations



Definition vector space:

The Attempt at a Solution



##(\alpha - \beta)\overrightarrow a = (\alpha + (-\beta))\overrightarrow a = \alpha \overrightarrow a + (-\beta) \overrightarrow a##

I'm stuck here, I want to show that:

##(-\beta) \overrightarrow a = -(\beta \overrightarrow a)##

I showed that both have the same symmetric element. But I did not show that symmetric elements are unique. Is there an easier way as this is an exercise and I think I'm overseeing something.

EDIT: I cannot use ##(-1)\overrightarrow a = - \overrightarrow a##

How you can or should do it depends on exactly what vector-space axioms you start with. I have seen different books using different systems of axioms, so being very precise at this point is important.

BTW: you cannot "proof" something, but you might be able to prove it, or to give a proof.
 
  • #17
Ray Vickson said:
How you can or should do it depends on exactly what vector-space axioms you start with. I have seen different books using different systems of axioms, so being very precise at this point is important.

BTW: you cannot "proof" something, but you might be able to prove it, or to give a proof.

V is a commutative group for addition and for the scalar multiplication we have:

##1\overrightarrow a = \overrightarrow a##
##(\alpha \beta)\overrightarrow a = \alpha (\beta \overrightarrow a)##
##(\alpha + \beta)\overrightarrow a = \alpha \overrightarrow a + \beta \overrightarrow a##
##\alpha (\overrightarrow a + \overrightarrow b) = \alpha \overrightarrow a + \alpha \overrightarrow b##

where ##\alpha, \beta \in \mathbb{K}, \overrightarrow a, \overrightarrow b \in V##
 

FAQ: Vector space properties: distributivity

What is distributivity in vector spaces?

Distributivity is a property of vector spaces that states that the distributive law holds for scalar multiplication and vector addition. This means that multiplying a scalar by the sum of two vectors is the same as the sum of the scalar multiplied by each individual vector.

How is distributivity different from commutativity in vector spaces?

Commutativity refers to the property that states the order of vector addition does not affect the final result, while distributivity refers to the property that states the order of scalar multiplication and vector addition does not affect the final result. In other words, commutativity involves only vector addition, while distributivity involves both scalar multiplication and vector addition.

Can distributivity be applied to any type of vector space?

Yes, distributivity is a fundamental property of vector spaces and can be applied to any type of vector space, including finite-dimensional, infinite-dimensional, and even abstract vector spaces.

How does distributivity contribute to vector space operations?

Distributivity is a crucial property in vector spaces as it allows for simplification and manipulation of vector equations. It also ensures that the operations of scalar multiplication and vector addition are well-defined and consistent within the vector space.

Can distributivity be proved mathematically?

Yes, distributivity can be proved using mathematical proofs and techniques. It can also be derived from the axioms and definitions of vector spaces, making it a fundamental property of vector spaces.

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