Proving V is a Vector Space: Simplifying the Process with Axiom Lemma

  • Thread starter Bachelier
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In summary, the conversation discusses a more efficient way to prove that a given set is a vector space by utilizing a lemma and identifying the encompassing vector space. It also mentions alternative methods such as checking for closure under scalar multiplication and vector addition, proving the set is a subspace of a larger vector space, or using a basis to show that a subset of vectors satisfies closure requirements.
  • #1
Bachelier
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Though this may be related to lin. alg. but it deals with Analysis.

There are 8 axioms for Vector Spaces. To prove a space ##V## is a VS, one must check all 8 axioms (i.e. closure under addition, scalar multi. etc...)

My question is this, it seems cumbersome to have to do this every time. Would it be better to use the lemma that states: "A non∅ subset ##W## or a VS ##V## is a subspace ##iff##

##\alpha v + \beta w \in W, \forall \ v, w \in W, \ \alpha , \beta \in \mathbb{F} \ \ (1)##​
where ##\mathbb{F}## is the field of scalars.

First, would it be correct to use this "Lemma"?

And second, what should the encompassing VS be: because the lemma states ##W## is a subspace of a VS ##V##, but if I want to prove a set

##G \subsetneq \mathbb{R \times R}## is a VS, I guess I should aim to show it is subspace of the Vector Space ##\mathbb{R \times R}##
 
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  • #2
If you know that a given set ##V## is already a vector space, and if ##W\subseteq V##, then all you need to check is that ##W## is closed under scalar multiplication and vector addition, which is what you wrote above. As for subspaces of ##\mathbb{R}^{2}##, if this is over the field ##\mathbb{R}## and under the usual scalar multiplication as well as vector addition then one can easily classify all non-trivial subspaces of ##\mathbb{R}^{2}## as lines through the origin. All you need to do is check that ##G## is a line through the origin or is a trivial subspace of ##\mathbb{R}^{2}##.
 
  • #3
For sure, it is tedious having to go through all 8 axioms. In any class beyond an intro to linear algebra class, you could simply say "it's easy to see that W is a subspace".

You can also phrase things in a slightly clever way (again, if your audience has the background):

* W is the image of a linear map f : X -> V (for some vectorspace X). It's easy to prove the image of a linear map is a subspace of the codomain.

* W is the kernel of a linear map f : V -> X (for some vectorspace X). Again, kernels are always subspaces.

* If you have a basis B = b1, b2, ..., bn for V, you can always take some subset B' of those b_i's and take W to be the span of B'. Spans are basically a way to satisfy all the closure requirements.
 
  • #4
Cool. Thank you.
 
  • #5


I appreciate your question about simplifying the process of proving that a given set is a vector space. While it may seem cumbersome to check all eight axioms every time, it is an essential step to ensure that the set satisfies all the necessary properties of a vector space. However, there are ways to streamline this process, such as using the lemma you mentioned.

The lemma you referenced is indeed correct and can be used to prove that a subset is a subspace of a given vector space. This lemma essentially combines several of the axioms into one statement, making the process more efficient. However, it is important to note that this lemma still requires you to check that the set satisfies all eight axioms, just in a more condensed form.

In terms of your specific example, if you want to prove that a set ##G## is a vector space, you should aim to show that it is a subspace of the vector space ##\mathbb{R \times R}##. This means that ##G## must satisfy all eight axioms, including the closure under addition and scalar multiplication property stated in the lemma.

In summary, while the lemma may simplify the process of proving a set is a vector space, it is still necessary to check all eight axioms. However, using the lemma can make this process more efficient and streamlined.
 

1. What is a VS?

A VS, or vector space, is a mathematical concept that represents a set of vectors that can be added and multiplied by scalars to produce other vectors within the same set.

2. How do you prove that V is a VS?

To prove that V is a VS, you must show that it satisfies the 10 axioms or properties of a vector space. These include properties such as closure under addition and scalar multiplication, existence of a zero vector, and distributivity.

3. What are some common examples of VS?

Some common examples of VS include Euclidean spaces, function spaces, and polynomial spaces. These are all sets of vectors that satisfy the axioms of a vector space.

4. Can V be a VS if it has infinite dimensions?

Yes, V can still be a VS even if it has infinite dimensions. In fact, many commonly used vector spaces, such as function spaces, have infinite dimensions. The key is that the vector space must satisfy the axioms, not have a specific number of dimensions.

5. How is a VS different from other mathematical objects like groups or rings?

A VS is different from groups or rings because it focuses specifically on the properties of addition and scalar multiplication. Groups and rings have different operations and different sets of axioms that they must satisfy in order to be considered valid mathematical objects.

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