Linear Algebra: Vectors/Proofs

In summary: So it is possible for x and y to be non-zero vectors with x \cdot y = 0 Such vectors are called "orthogonal" . You can see this for yourself by looking at (1,0) \cdot (0,1) The important point here is not that there is some vector w such that w \cdot (u-v) = 0 but that you get 0 for every possible w.
  • #1
trulyfalse
35
0
Hello PF!

Homework Statement


Prove the following: if u and v are two vectors in Rn such that u[itex]\cdot[/itex]w = v[itex]\cdot[/itex]w for all wεRn , then we have u = v

Homework Equations





The Attempt at a Solution


u[itex]\cdot[/itex]w - v[itex]\cdot[/itex]w = 0
w[itex]\cdot[/itex](u - v) = 0

I'm not sure what to do after applying the distributive property (in reverse). How do I go about proving that the vectors u and v are equal? I considered establishing two cases in which w = 0 and u-v = 0 but that doesn't help me out. Are there any properties that I can use to construct this proof?
 
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  • #2
trulyfalse said:
Hello PF!

Homework Statement


Prove the following: if u and v are two vectors in Rn such that u[itex]\cdot[/itex]w = u[itex]\cdot[/itex]v for all wεRn , then we have u = v

Homework Equations





The Attempt at a Solution


u[itex]\cdot[/itex]w - v[itex]\cdot[/itex]w = 0
w(u[itex]\cdot[/itex]v) = 0

I'm not sure what to do after applying the distributive property (in reverse). How do I go about proving that the vectors u and v are equal? I considered establishing two cases in which w = 0 and u-v = 0 but that doesn't help me out. Are there any properties that I can use to construct this proof?

You've got some typos in there. I'd fix them. But pick w=(u-v). If (u-v).(u-v)=0 then what can you say about (u-v). Look at the properties of the dot product.
 
  • #3
Another way to think of it is this: [tex] w \cdot (u-v) = 0 [/tex] for every choice of w. What can you say about a vector t whose inner product with any vector whatever is 0? Given a particular vector t with non-zero components, you can always find a vector w such that [tex] w \cdot t \ne 0 [/tex] For example w = t.

So if u-v has any non-zero components, what can you conclude?

That word "every" is very powerful.
 
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  • #4
If the vectors are non-zero, then we can conclude that u-v must be equal to zero, and therefore u=v. We can also conclude that vectors w and (u-v) must be orthogonal because their dot product is equal to zero. However, I am not sure how to prove that the vectors are nonzero. Am I supposed to assume that they are and work from there?

EDIT: Never mind, I've just realized (after reading bmath's post more carefully) that this represents EVERY choice of w. So if w does not equal zero, then u-v must be equal to zero to produce a dot product of zero. Therefore, u = v. If there are any gaps in my understanding please let me know. Thanks for your help guys.
 
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  • #5
trulyfalse said:
If the vectors are non-zero, then we can conclude that u-v must be equal to zero, and therefore u=v. We can also conclude that vectors w and (u-v) must be orthogonal because their dot product is equal to zero. However, I am not sure how to prove that the vectors are nonzero. Am I supposed to assume that they are and work from there?

EDIT: Never mind, I've just realized (after reading bmath's post more carefully) that this represents EVERY choice of w. So if w does not equal zero, then u-v must be equal to zero to produce a dot product of zero. Therefore, u = v. If there are any gaps in my understanding please let me know. Thanks for your help guys.

That's a little confused. You've got w.(u-v)=0 for EVERY w. So you can pick w=(u-v). So now you've got (u-v).(u-v)=0. You should have a property that tells you that for a vector V, V.V=0 if and only if V=0. So?
 
  • #6
see answer below
 
  • #7
it is perfectly possible for x and y to be non-zero vectors with [tex]x \cdot y = 0[/tex] Such vectors are called "orthogonal" . You can see this for yourself by looking at [tex] (1,0) \cdot (0,1) [/tex] The important point here is not that there is some vector w such that [tex] w \cdot (u-v) = 0 [/tex] but that you get 0 for every possible w.

There is in fact a property that tells you if [tex] v \cdot\ v = 0 [/tex] then v = 0. The dot product of v with itself is the square of the length of the vector. If it is 0 you have a vector of length 0 -- i.e. the zero vector.
 

1. What is a vector in linear algebra?

A vector in linear algebra is a mathematical object that has both magnitude and direction. It is represented by an ordered list of numbers and is commonly denoted by a bold lowercase letter (e.g. v) or an arrow above the letter (e.g. →v). Vectors are often used to represent physical quantities such as velocity, force, and displacement.

2. How do you add and subtract vectors?

To add or subtract two vectors, you simply add or subtract the corresponding components of the vectors. For example, if v = (2, 3) and w = (5, -1), then v + w = (2+5, 3+(-1)) = (7, 2) and v - w = (2-5, 3-(-1)) = (-3, 4).

3. What is a linear combination of vectors?

A linear combination of vectors is when you multiply each vector by a scalar (a real number) and then add the resulting vectors together. For example, if v = (2, 3) and w = (5, -1), then 3v + 2w = (3·2, 3·3) + (2·5, 2·(-1)) = (6, 9) + (10, -2) = (6+10, 9+(-2)) = (16, 7).

4. What is a matrix in linear algebra?

A matrix in linear algebra is a rectangular array of numbers (or other mathematical objects) that is used to represent a linear transformation between two vector spaces. It is commonly denoted by a bold uppercase letter (e.g. A) and is made up of rows and columns of numbers. Matrices are used to solve systems of linear equations and perform other operations in linear algebra.

5. How do you prove if two vectors are linearly dependent or independent?

To prove if two vectors are linearly dependent or independent, you can use the definition of linear independence: v and w are linearly independent if and only if the only solution to the equation av + bw = 0 is a = b = 0. In other words, if the only way to get a linear combination of the two vectors to equal the zero vector is by setting the scalars to zero, then the vectors are linearly independent. If there exists a non-zero solution, then the vectors are linearly dependent.

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