Proving Linear Independence

In summary, the reasoning above is valid for any scalar product, so it is true that for any scalar product these vectors are linearly independent.
  • #1
A.Magnus
138
0
In a problem I am working on, it is given that $V_1, V_2, ... , V_n$ are mutually perpendicular vectors in a space defined with a certain scalar product. I need to prove or disprove that $V_i$ are linearly independence regardless of any definition of scalar product.

I think the solution should go like these: Assume that the vectors are linearly independent such that there exist number $c_i$, not all of them being trivial, so that $c_1V_1 + c_2V_2 + ... c_iV_i + ... + c_nV_n = 0.$ And then
$$\begin{align}
V_i (c_1V_1 + c_2V_2 + ... c_iV_i + ... + c_nV_n) &= 0\\
c_iV_iV_i &=0\\
\end{align}$$

Am I on the right track? How do I tie this up to proving or disproving the claim? I am lost on writing it mathematically. Many thanks in advance for your gracious help. ~MA
 
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  • #2
MaryAnn said:
In a problem I am working on, it is given that $V_1, V_2, ... , V_n$ are mutually perpendicular vectors in a space defined with a certain scalar product. I need to prove or disprove that $V_i$ are linearly independence regardless of any definition of scalar product.

I think the solution should go like these: Assume that the vectors are linearly independent such that there exist number $c_i$, not all of them being trivial, so that $c_1V_1 + c_2V_2 + ... c_iV_i + ... + c_nV_n = 0.$ And then
$$\begin{align}
V_i (c_1V_1 + c_2V_2 + ... c_iV_i + ... + c_nV_n) &= 0\\
c_iV_iV_i &=0\\
\end{align}$$

Am I on the right track? How do I tie this up to proving or disproving the claim? I am lost on writing it mathematically. Many thanks in advance for your gracious help. ~MA

Hi again MaryAnn! ;)

You're already there.
If not all numbers $c_i$ are zero, there must be at least one that is non-zero, let's say $c_i \ne 0$.
Furthermore, one of the requirements for an inner product is that $\langle \mathbf v, \mathbf v \rangle > 0$ iff $\mathbf v \ne \mathbf 0$.
Since we have $c_i\mathbf V_i\mathbf V_i =0$ we have a contradiction.
Therefore the $\mathbf V_i$ are linearly independent.
 
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  • #3
I like Serena said:
Hi again MaryAnn! ;)

You're already there.
If not all numbers $c_i$ are zero, there must be at least one that is non-zero, let's say $c_i \ne 0$.
Furthermore, one of the requirements for an inner product is that $\langle \mathbf v, \mathbf v \rangle > 0$ if $\mathbf v \ne \mathbf 0$.
Since we have $c_i\mathbf V_i\mathbf V_i =0$ we have a contradiction.
Therefore the $\mathbf V_i$ are linearly independent.

Thank you for your gracious help! But the problem is not asking for linear independence, the question is asking "Is it true that for any scalar product these vectors are linearly independent?" The solution I wrote you above may mis-lead you to think that the problem is asking for linear independence but it is not. Sorry for any misunderstanding! It is my bad! Thank you again. ~MA
 
  • #4
MaryAnn said:
Thank you for your gracious help! But the problem is not asking for linear independence, the question is asking "Is it true that for any scalar product these vectors are linearly independent?" The solution I wrote you above may mis-lead you to think that the problem is asking for linear independence but it is not. Sorry for any misunderstanding! It is my bad! Thank you again. ~MA

The reasoning above is valid for any scalar product.
So it is true that for any scalar product these vectors are linearly independent.
What am I missing? :confused:
 
  • #5
I like Serena said:
The reasoning above is valid for any scalar product.
So it is true that for any scalar product these vectors are linearly independent.
What am I missing? :confused:

Now you got it right! Is there mathematical detail that you may want to suggest, instead of just writing "... the $V_i$ are linear independent for any scalar product..."? My prof is notoriously very picky about detail. Sorry to take off your time answering my question again. ~MA
 
  • #6
MaryAnn said:
Now you got it right! Is there mathematical detail that you may want to suggest, instead of just writing "... the $V_i$ are linear independent for any scalar product..."? My prof is notoriously very picky about detail. Sorry to take off your time answering my question again. ~MA

Sorry, no more detail to add.
(Oh, do make sure you write "Assume... dependent" instead of "Assume... independent" in your opening post if we're talking about being nitpicky.)
I believe it is full-proof, so I'd be interested in your prof finding something (else) to nitpick about. (Wink)
 
  • #7
I like Serena said:
Sorry, no more detail to add.
(Oh, do make sure you write "Assume... dependent" instead of "Assume... independent" in your opening post if we're talking about being nitpicky.)
I believe it is full-proof, so I'd be interested in your prof finding something (else) to nitpick about. (Wink)

Thank you again, sorry for my dumb question. I hope it does not dumbfound you! ~MA
 

1. What is linear independence?

Linear independence refers to the relationship between a set of vectors in a vector space. It means that no vector in the set can be written as a linear combination of the other vectors in the set. In other words, no vector is redundant or unnecessary in the set.

2. Why is it important to prove linear independence?

Proving linear independence is important because it allows us to determine if a set of vectors spans the entire vector space. If a set of vectors is linearly independent, it means that they form a basis for the vector space and can be used to represent any vector within that space. This is crucial in many areas of science and engineering, such as in solving systems of equations and in constructing mathematical models.

3. How do you prove linear independence?

To prove linear independence, you must show that the only solution to the equation ax + by + cz + ... = 0, where a, b, c, etc. are coefficients and x, y, z, etc. are the vectors in the set, is when all the coefficients are equal to 0. This can be done through various methods, such as using Gaussian elimination or by constructing a matrix and finding its determinant.

4. Can a set of two vectors be linearly independent?

Yes, a set of two vectors can be linearly independent if they are not parallel to each other. This means that they cannot be scaled versions of each other, and thus, one vector cannot be written as a multiple of the other. In three-dimensional space, for example, two non-parallel vectors will span the entire space and therefore be linearly independent.

5. Are there any visual representations of linear independence?

Yes, linear independence can be visually represented through geometric concepts such as linear combinations, span, and the direction of vectors. For example, if two vectors are linearly independent, they will point in different directions and will not lie on the same line. In contrast, if they are linearly dependent, they will lie on the same line and will have the same direction.

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