Proving the Vector Space Property: cv = 0, v ≠ 0 → c = 0

In summary: So c must have been 0, after all.In summary, if given a field F and a vector space V, where c \in F and v \in V, the statement "cv = 0, v \neq 0 \Rightarrow c = 0" holds true. This can be proven by assuming the opposite and using the properties of vector spaces and fields to arrive at a contradiction. Therefore, it can be concluded that in order for cv to equal 0, c must be equal to 0 in this scenario.
  • #1
bjgawp
84
0
I'm considering the problem: Given [tex]c \in \bold{F}[/tex], [tex]v \in V[/tex] where F is a field and V a vector space, show that [tex]cv = 0, v \neq 0 \ \Rightarrow \ c = 0 [/tex]

I've been wrapping my head around this one for a while now but I can't seem to get it. Proving that if cv = 0 and v [tex]\neq[/tex] 0 implies v = 0 is easy since we can simply multiply by [tex]c^{-1}[/tex] but in vector space, we don't have that kind of inverse for vectors seeing how we only have scalar multiplication.
 
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  • #2
But can you not simply multiply by the inverse of c to get a contradiction - that is v=0?
 
  • #3
bjgawp said:
I'm considering the problem: Given [tex]c \in \bold{F}[/tex], [tex]v \in V[/tex] where F is a field and V a vector space, show that [tex]cv = 0, v \neq 0 \ \Rightarrow \ c = 0 [/tex]

I've been wrapping my head around this one for a while now but I can't seem to get it. Proving that if cv = 0 and v [tex]\neq[/tex] 0 implies v = 0 is easy since we can simply multiply by [tex]c^{-1}[/tex] but in vector space, we don't have that kind of inverse for vectors seeing how we only have scalar multiplication.
Since c is NOT a vector that doesn't matter!
 
  • #4
When stuck, think about the components. It will get you more stuck or get you an easy proof.
 
  • #5
A "fancy" way to do it if you don't like the coordinate way is to take the alternate definition of a vector space as an embedding of a field into the endomorphism ring of an abelian group. The map for the embedding is just:

[tex] \theta: F \rightarrow End(A), c \rightarrow cI[/tex]

Where I is the identity matrix. Then if [tex] v \in A[/tex] what we normally write as [tex]cv[/tex] is just [tex] \theta(c)(v)[/tex] and you can use that to read off all of the vector space properties.

Then [tex] \theta(c)(v) = 0[/tex] means that [tex] v \in Ker(\theta(c))[/tex] and that should give you the result that you're looking for considering that [tex] \theta(c)[/tex] is invertible by virtue of being in a subfield of End(A).

EDIT: I should note that you can't use the map that I gave you in the definition because we're conflating the Matn(A) with End(A) but they are isomorphic. I gave it to you so that you would know what the map is. In reality, you would prove that the map induces a ring homomorphism as advertised. This is just a simple exercise. To take it as the definition of a vector space, you assume that the embedding is given.
 
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  • #6
Suppose cv=0, v =/= 0

then take a new scalar from F (call it a)

a*(cv)=a*0
(ac)(v)=0
ca(v)=0
c(av)=0

Then c * (the whole vector space spanned by v) = 0
Since this is a vector space, c must be 0 (I forget the name of this property).
 
  • #7
the vector spaces are over fields, the components of a vector are elements of the field
 
  • #8
I guess Vee's method is the simplest:

Assume, by contradiction, that [tex]cv=0[/tex] and [tex]v\neq 0[/tex], but [tex]c\neq 0[/tex]. Then the inverse [tex]c^{-1}[/tex] exists, and multiplying [tex]cv=0[/tex] to the left with it we get [tex]c^{-1}cv=c^{-1}0[/tex], i.e. [tex]v=0[/tex], in contradiction with [tex]v\neq 0[/tex].
 

1. What is the vector space property?

The vector space property is a set of mathematical rules that define the characteristics of a vector space, which is a set of objects (vectors) that can be added together and multiplied by a scalar (a number). These rules include closure under addition and scalar multiplication, associativity, commutativity, and the existence of an additive identity and inverse.

2. Why is the vector space property important?

The vector space property is important because it allows us to perform mathematical operations on vectors in a consistent and meaningful way. It also allows us to apply linear algebra to a wide range of problems in physics, engineering, economics, and other fields.

3. How is the vector space property different from other mathematical properties?

The vector space property is different from other mathematical properties in that it specifically applies to vector spaces, which are sets of objects with specific rules for addition and scalar multiplication. Other properties, such as the commutative and associative properties, apply to a wider range of mathematical operations.

4. Can the vector space property be violated?

Yes, the vector space property can be violated if one or more of the rules is not satisfied. For example, if the rule of closure under addition is violated, then the result of adding two vectors may not be a vector. This can happen if the vectors are in different dimensions or if they do not have the same units.

5. How is the vector space property used in real-world applications?

The vector space property is used in many real-world applications, such as image and signal processing, data compression, and machine learning. It provides a framework for representing and manipulating data in a structured and efficient manner. For example, in image processing, images can be represented as vectors in a high-dimensional vector space, and the vector space property allows us to apply operations such as scaling, rotation, and translation to the images.

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