Showing Uniqueness of Elements of a Vector Space

In summary, the homework statement is trying to find a solution to an equation that requires two sums to be denoted by a unique value. The two sums are denoted by X1 X2, but it is impossible to add vectors to both sides of the equation to get a solution. The only solution is when X1=X2.
  • #1
TranscendArcu
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Homework Statement



http://img267.imageshack.us/img267/8924/screenshot20120118at121.png

The Attempt at a Solution

We have that X = A + B. To show that X is unique, let two such sums be denoted by X1 X2 such that X1X2. We write,

X1 = A + B
X2 = A + B

The equations imply,

X1 - A - B = 0
X2 - A - B = 0

Which imply,

X1 - A - B = X2 - A - B. If we add vectors to both sides,

X1 - A - B + A + B = X2 - A - B + A + B

X1 + 0 + 0 = X2 + 0 + 0
X1 = X2, which contradicts our assertion that X1X2. This shows that such an X is unique.
 
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  • #2
don't you mean A+X=B?
 
  • #3
and though on the right track 3 points,
- i think you need to justify the minus operation by invoking the existence of an additive inverse
- eqauting to zero is an unecessary step, i would start from B=B in this case
- you need to show both existence and uniqueness
 
  • #4
Yeah, you're totally right. I just typed X =A + B by accident. As for proving the existence of such an X, how do I begin that? I think proving uniqueness should be, with some adjustment, what I have in #1.
 
  • #5
just find a vector that exists that satisfies the requirement
A+X=B
A+X+(-A)=B+(-A)
X+A+(-A)=B+(-A)
X+0=B+(-A)
X=B+(-A)

So somewhat obviously B+(-A) does the job
 
  • #6
its a little mechanical, but each one of the above operations is justified by a vector space axiom
 
  • #7
Okay, that's pretty clear. Thanks!

Another question I have is why the set of polynomials of degree = 2 is not a vector space. It isn't obvious to me which axiom is not being satisfied here. Could you recommend an axiom to examine more closely?
 
  • #8
does it contain the zero vector?
 
  • #9
So I understood that degree = 2 indicated polynomials of the form [itex]a_0 + a_1 x +a_2 x^2[/itex] if [itex]a_0[/itex],[itex]a_1[/itex],[itex]a_2 = 0[/itex], then certainly the entire polynomial goes to zero.

I doubt, somehow, that this is the conclusion I am supposed to arrive at. Does degree = 2 indicate rather polynomials of the form [itex]a_2 x^2[/itex]? But even if this is so, I still think I could let the coefficient be zero to get a zero vector.

What does "degree = 2" actually mean?
 
  • #10
I would interpret degree=2 as [itex]a_2\neq0 [/itex]..

And degree<=2 as putting no constraints on a_0,a_1,a_2... but that's just my interpretation

Its also probably biased by that I'm pretty sure all polynomials with degree<=2 is a vector space
 
  • #11
Also the sum of the two second degree polynomials, [itex]1+ x- x^2[/itex] and [itex]2- 2x+ x^2[/itex], is [itex]3- x[/itex] which is not a second degree polynomial.

(The set of all polynomials of degree less than or equal to is a vector space.)
 

FAQ: Showing Uniqueness of Elements of a Vector Space

How do you define uniqueness of elements in a vector space?

Uniqueness of elements in a vector space refers to the property that each element can be represented by a unique combination of scalars (coefficients) and basis vectors. In other words, no two elements in a vector space can be identical.

What is the significance of showing uniqueness of elements in a vector space?

Showing uniqueness of elements in a vector space is essential because it helps to understand the structure and properties of the vector space. It also allows us to define operations and transformations on the elements of the vector space accurately.

How do you prove the uniqueness of elements in a vector space?

The uniqueness of elements in a vector space can be proven by showing that any element in the vector space can be expressed as a unique linear combination of basis vectors. This can be done using the properties of vector addition and scalar multiplication.

Can uniqueness of elements be demonstrated in any vector space?

Yes, the uniqueness of elements is a fundamental property of all vector spaces, regardless of their dimension or basis. It is a defining characteristic of vector spaces and is essential in defining vector operations and transformations.

How does the uniqueness of elements relate to linear independence?

The uniqueness of elements in a vector space is closely related to linear independence. A set of vectors is linearly independent if no vector in the set can be expressed as a linear combination of the other vectors. This property ensures that each element in the vector space is unique and cannot be represented by a linear combination of other elements.

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