Basis of Vectors: Definition & Examples

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    Basis Vectors
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Discussion Overview

The discussion centers around the concept of a basis in vector spaces, exploring its definition, properties, and examples. Participants engage in explaining the requirements for a set of vectors to qualify as a basis, including linear independence and spanning properties, while also providing examples from different dimensions.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • Some participants assert that a basis must consist of linearly independent vectors that span the vector space.
  • It is proposed that any vector in a vector space can be expressed uniquely as a linear combination of the basis vectors.
  • Examples are provided to illustrate valid bases in R² and R³, including specific sets of vectors that do or do not form a basis.
  • One participant questions whether there are exceptions to the uniqueness of representation in terms of a basis, leading to further clarification that different bases can yield different representations for the same vector.
  • A later reply discusses the implications of a basis for describing transformations and the uniqueness of representation, providing a mathematical argument to support the claim that a basis ensures unique linear combinations.

Areas of Agreement / Disagreement

Participants generally agree on the definition and properties of a basis, but there are nuances regarding the uniqueness of vector representation and the implications of different bases, which remain somewhat contested.

Contextual Notes

Some participants express uncertainty about the implications of having multiple bases and how that affects the uniqueness of vector representations. There are also discussions about the conditions under which certain sets of vectors do not form a basis, highlighting the need for clarity on linear independence and spanning sets.

SherlockOhms
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So, I know that for a set of vectors to be a basis the set of vectors must be linearly independent and also must be a spanning set of vectors. So, they can't be parallel. I still feel that I'm not fully understanding what a basis is. Could someone explain to me, maybe with an example, what is a basis? Thanks.
 
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A basis allows you to represent any vector in the vector space as a unique linear combination of the basis vectors in said basis. This is the main utility of a basis. For example you can represent any vector in ##\mathbb{R}^{3}## as a unique linear combination of the basis vectors ##e_{1} = (1,0,0)^T, e_{2} = (0,1,0)^{T}, e_{3} = (0,0,1)^{T}##.
 
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In R2:

{(1,0),(0,1)} is a basis. Given a vector (a,b) I can write it as a(1,0)+b(0,1) uniquely.

{(1,0),(1,1)} is a basis. Given a vector (a,b) I can write it as (a-b)*(1,0)+b*(1,1) and this is the only way to do it.

{(1,0)} is not a basis because it does not span the set of vectors

{(1,0),(2,4),(-1,1)} is not a basis because the vectors are linearly dependent. I can write (2,4) = 4*(-1,1)+6*(1,0)In R3 some examples of bases:
{(1,0,0),(0,1,0),(0,0,1)}
{(1,0,0),(1,1,0),(1,1,1)}
{(-1,14,12),(2,0,11),(65.3,114,-9)}

The last one might not be immediately obvious that it is a basis, but the first and second one you should be able to prove. Some examples that are not bases:

{(1,0,0),(14,12,10)}. This doesn't have enough vectors to span R3 (once you know the size of one basis is 3, all bases must be size 3), so it can't be a basis. After a little thought you should be able to explain why the vector (0,1,0) is not in the span of these two vectors.

{(1,0,0),(0,1,0),(1,1,0)} has the right number of vectors, but is still not a basis. (1,1,0) is in the span of (1,0,0) and (0,1,0) so the vectors are not linearly independent, and they also are not a spanning set - (0,0,1) is not in the span.

{(1,0,0),(0,1,0),(1,1,1),(1245,-9034,1234)} Has too many vectors, so cannot be a basis. You should be able to express (1245,-9034,1234) as a linear combination of the other three vectors. This one is at least a spanning set though, if that counts for anything
 
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Thanks for that, lads. So, any arbitrary vector in R^3 can only be expressed in one way only in terms the 3 other vectors which form the basis of R^3? There's never an exception to this?
 
SherlockOhms said:
Thanks for that, lads. So, any arbitrary vector in R^3 can only be expressed in one way only in terms the 3 other vectors which form the basis of R^3? There's never an exception to this?
By definition, any vector in R^3 MUST have a unique linear expression in terms of a chosen basis for R^3. Now the basis that I wrote above isn't the only basis you can pick for R^3. There are many bases you can pick so a given vector in R^3 has many different expansions in terms of different bases.
 
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Right, I've got it now. Thanks again.
 
A basis also allows you to describe a transformation fully by describing its effect on basis vectors
(this is what a matrix does, w.respect to linear transformations). Given that in most cases (we're excluding
here, e.g., vector spaces over finite fields) your spaces will contain infinitely-many vectors, this is a big
plus.

Re the uniqueness, assume (using a fixed basis for R^3 for definiteness; I think the generalization to other finite-dimensional vector spaces is clear) the representation in terms of a fixed basis {v1,v2,v3} is not unique, so that we can write some v as:

v=a1*v1+a2*v2+a3*v3 , and as:

v=c1*v1+c2*v2+c3*v3

For triples (a1,a2,a3) , (c1,c2,c3) of scalars in your bases field; and neither triple has all zeros.

Now, subtract one representation of v from the other, to get:

(a1-c1)*v1+(a2-c2)*v2+(a3-c3)*v3=0

Since the individual triples are not all zeros, this difference is a non-zero linear
combination of basis vectors that gives you a zero. This is a contradiction of the
assumption that {v1,v2,v3} is a basis.
 

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