## Linear Independent vector

Hi

I'm presented with a set of vectors in $\mathbb{R}^{3}$

$v_1 = (a_1,a_2,a_3)$ $v_2 = (b_1,b_2,b_3)$ and $v_3 = (c_1,c_2,c_3)$

I'm suppose to determain if these vectors are linear independent.

My question is: The most simple way of proving the above is that written the vectors as a set of linear combinations ???

$a_1 x + a_2 y + a_3 z = 0$
$b_1 x + b_2 y + b_3 z = 0$
$c_1 x + c_2 y + c_3 z = 0$

/Fred
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 Quote by Mathman23 My question is: The most simple way of proving the above is that written the vectors as a set of linear combinations ??? $a_1 x + a_2 y + a_3 z = 0$ $b_1 x + b_2 y + b_3 z = 0$ $c_1 x + c_2 y + c_3 z = 0$
What do you suppose linear independence would imply for the solution to those equations?

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Homework Help
 Quote by Mathman23 Hi I'm presented with a set of vectors in $\mathbb{R}^{3}$ $v_1 = (a_1,a_2,a_3)$ $v_2 = (b_1,b_2,b_3)$ and $v_3 = (c_1,c_2,c_3)$ I'm suppose to determain if these vectors are linear independent. My question is: The most simple way of proving the above is that written the vectors as a set of linear combinations ??? $a_1 x + a_2 y + a_3 z = 0$ $b_1 x + b_2 y + b_3 z = 0$ $c_1 x + c_2 y + c_3 z = 0$ /Fred
This looks like you are setting your three vectors to zero. That is not correct. What you need to show is that there is no linear combination of the three vectors that will add to zero unless the coefficients multiplying the three vectors (not their internal components) are individually zero.

$Av_1 + Bv_2 + Cv_3 = 0$

if and only if A = B = C = 0

## Linear Independent vector

 Quote by SpaceTiger What do you suppose linear independence would imply for the solution to those equations?

How I have understood Linear Independence is that in order to prove if a set of vectors are linear linear independent, then one has to write this set as linear combinations, where each lin.comb is set to equal cero.

Then if there exist a solution for the set of linear combinations then the previously mentioned vectors are linear independent.

Is that wrong?

/Fred

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 Quote by Mathman23 Then if there exist a solution for the set of linear combinations then the previously mentioned vectors are linear independent. Is that wrong?
It's right, if you alter a bit to say that if there exists a non-trivial solution to the set of equations, then they're linearly independent. The trival solution would involve just setting x, y, and z to 0.

So do you know how you can you figure out if there are non-trivial solutions?

Hi

$x,y,z$ are not zero. Its each of the linear combinations who I have set to equal zero.

/Fred

 Quote by OlderDan This looks like you are setting your three vectors to zero. That is not correct. What you need to show is that there is no linear combination of the three vectors that will add to zero unless the coefficients multiplying the three vectors (not their internal components) are individually zero. $Av_1 + Bv_2 + Cv_3 = 0$ if and only if A = B = C = 0

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 Quote by Mathman23 $x,y,z$ are not zero. Its each of the linear combinations who I have set to equal zero.
It's being assumed that you already know ax, bx, and cx. When you set it up in equation form like that, it implies that you are indeed solving for x, y, and z. One solution to the equations is that they are all equal to zero, but there may be other solutions. Whether or not there are depends on the values of ax, bx, and cx.

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 Quote by Mathman23 Hi $x,y,z$ are not zero. Its each of the linear combinations who I have set to equal zero. /Fred
I think this explains your confusion. You are thinking of x, y, and z, as possibly linearly independent and trying to show that a linear combination of x, y, and z can only be zero if the coefficients are zero. That is true, but that is not the issue here. Take it as given that x, y, and z ARE linearly independent. Now you are forming three other vectors using x, y, and z as a linearly independent basis for representing those three vectors. By non-trivial, what Space Tiger means is that those three vectors are NOT themselves zero. At least one of the coefficients in each vector is non-zero. That is given.

You could construct three vectors by taking linear combinations of x, y, and z that are not linearly independent. For example, if you added some multiples of your first two vectors together to make the third, you would have three vectors, but they would not be independent. If you took the negative of the third vector and added it to those same multiples of the first two vectors you would get zero, and that would prove that your three vectors are not independent.

It might help you to reduce the problem to two dimensions and look at

$Av_1 + Bv_2$

where

$v_1 = a_1x + a_2y$ , $v_2 = b_1x + b_2y$

Draw some vectors in two dimensions that are not parallel to one another, and see if there is any way multiples of those vectors can add to zero. It cannot be done, unless both vectors are multiplied by zero. Now try to make a third vector

$v_3 = c_1x + c_2y$

that cannot be formed by adding multiples of the first two vectors. That also cannot be done. There is no way you can have three linearly independent vectors in two dimensions, but there are infinitely many pairs of two linearly independent vectors in two dimensions.

Now look again at your three dimensional problem. You have three non-zero vectors. They might be linearly independent, and they might not. They could all be multiples of the same vector. What you are trying to show here is that there is no way one of your three vectors can be written as a linear combination of the other two. None of your vectors are zero, and there is no combination of those vactors that is zero UNLESS all three coefficients in the combination of those three vectors is zero.

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 Quote by Mathman23 Hi I'm presented with a set of vectors in $\mathbb{R}^{3}$ $v_1 = (a_1,a_2,a_3)$ $v_2 = (b_1,b_2,b_3)$ and $v_3 = (c_1,c_2,c_3)$ I'm suppose to determain if these vectors are linear independent. My question is: The most simple way of proving the above is that written the vectors as a set of linear combinations ??? $a_1 x + a_2 y + a_3 z = 0$ $b_1 x + b_2 y + b_3 z = 0$ $c_1 x + c_2 y + c_3 z = 0$ /Fred
The following summarizes and clarifies info already presented or referenced in this thread.

Define three vectors {V1, V2, V3 $$\mathbb{R}^{3}$$} by the following:
(Note: This definition differs from OP's.)

$$1: \ \ \ \ \vec{V}_{1} \ = \ \left [ \begin{array}{ccc} a_{1} \\ b_{1} \\ c_{1} \\ \end{array} \right ] \ \ \ \ \ \ \vec{V}_{2} \ = \ \left [ \begin{array}{ccc} a_{2} \\ b_{2} \\ c_{2} \\ \end{array} \right ] \ \ \ \ \ \ \vec{V}_{3} \ = \ \left [ \begin{array}{ccc} a_{3} \\ b_{3} \\ c_{3} \\ \end{array} \right ]$$

These vectors are Linearly Independent if and only if the scalar triple (x, y, z)=(0, 0, 0) is the SINGLE UNIQUE solution to:

$$2: \ \ \ \ x\vec{V}_{1} \ + \ y\vec{V}_{2} \ + \ z\vec{V}_{3} \, \ = \ \, \vec{\mathbf{0}}$$

Or equivalently, the single unique solution to:

$$3: \ \ \ \ \ x\left [ \begin{array}{ccc} a_{1} \\ b_{1} \\ c_{1} \\ \end{array} \right ] \ + \ y\left [ \begin{array}{ccc} a_{2} \\ b_{2} \\ c_{2} \\ \end{array} \right ] \ + \ z\left [ \begin{array}{ccc} a_{3} \\ b_{3} \\ c_{3} \\ \end{array} \right ] \ \ = \ \ \left [ \begin{array}{ccc} 0 \\ 0 \\ 0 \\ \end{array} \right ]$$

Or equivalently, that (x, y, z)=(0, 0, 0) is the single unique solution to:

$$4: \ \ \ \ \begin{array}{ccccccc} a_1 x & + & a_2 y & + & a_3 z & \, = \, & 0 \\ b_1 x & + & b_2 y & + & b_3 z & \, = \, & 0 \\ c_1 x & + & c_2 y & + & c_3 z & \, = \, & 0 \\$$

The triple (x, y, z) will be the single unique solution to Eq #4 if and only if the determinant of its coefficients is NON-zero:

$$5: \color{red}\ \ \ \ \ \ \left | \begin{array}{ccc} a_{1} & a_{2} & a_{3} \\ b_{1} & b_{2} & b_{3} \\ c_{1} & c_{2} & c_{3} \\ \end{array} \right | \ \ \neq \ \ \textsf{0} \ \ \ \color{black} \Longleftrightarrow \ \ \ \color{red} \{\vec{V}_{1}, \ \vec{V}_{2}, \ \vec{V}_{3} \ \ \ \textsf{Linearly Independent} \}$$

Eq #5 probably provides the easiest method for determining Linear Independence in $\mathbb{R}^{3}$.
For convenience, Left Side of Eq #5 is rapidly determined from:

$$6: \ \ \ \ \left [ \begin{array}{ccc} a_{1} & a_{2} & a_{3} \\ b_{1} & b_{2} & b_{3} \\ c_{1} & c_{2} & c_{3} \\ \end{array} \right ] \ \ = \ \ \left [ \ \vec{V}_{1} \ \ \vec{V}_{2} \ \ \vec{V}_{3} \ \right ]$$

Note that only 3 possibilities exist for Eq #4 solutions:
1) Single, Unique Solution
2) Infinite # of Solutions
3) No Solutions
Eq #5 indicates situation #1.

~~

 It's right, if you alter a bit to say that if there exists a non-trivial solution to the set of equations, then they're linearly independent.
I think you mean the oppsite! If there are non-trivial solutions, the vectors are dependent. The converse is also true.

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 Quote by Data ... If there are non-trivial solutions, the vectors are dependent. The converse is also true.
For the homogeneous case, if 1 non-trivial solution exists, then an infinite number exist (and of course the vectors are dependent).

It should also be noted that Linear Independence for given vectors {V1, V2, V3 $$\mathbb{R}^{3}$$} is usually stated in terms of:

$$2: \ \ \ \, \ \ \ \ \ \ x\vec{V}_{1} \ \ + \ \ y\vec{V}_{2} \ \ + \ \ z\vec{V}_{3} \ \ \, = \, \ \ \vec{\mathbf{0}} \ \ \ \ \ \ \ \ \textrm{ (solve for Real scalars x, y, z) }$$

In fact, any Real CONSTANT vector can appear on the Right Side:

$$2a: \ \ \ \ x\vec{V}_{1} \ + \ y\vec{V}_{2} \ + \ z\vec{V}_{3} \ \ = \ \ \left [ \begin{array}{c} 261 \\ -38.2 \\ 7 \\ \end{array} \right ]$$

The necessary and sufficient condition for Linear Independence is that Eq #2a (like that for Eq #2) has a SINGLE UNIQUE solution triple (x, y, z). (There's no need to actually find it.)

Again, the necessary and sufficient condition for a single unique solution of Eq #2a (like that for Eq #2) is that the determinant of its coefficients be NON-zero:
(See Msg #9 for more details.)

$$5: \color{red}\ \ \ \ \ \ \left | \begin{array}{ccc} a_{1} & a_{2} & a_{3} \\ b_{1} & b_{2} & b_{3} \\ c_{1} & c_{2} & c_{3} \\ \end{array} \right | \ \ \neq \ \ \textsf{0} \ \ \ \color{black} \Longleftrightarrow \ \ \ \color{red} \{\vec{V}_{1}, \ \vec{V}_{2}, \ \vec{V}_{3} \ \ \ \textsf{Linearly Independent} \}$$

where:

$$1: \ \ \ \ \vec{V}_{1} \ = \ \left [ \begin{array}{ccc} a_{1} \\ b_{1} \\ c_{1} \\ \end{array} \right ] \ \ \ \ \ \ \vec{V}_{2} \ = \ \left [ \begin{array}{ccc} a_{2} \\ b_{2} \\ c_{2} \\ \end{array} \right ] \ \ \ \ \ \ \vec{V}_{3} \ = \ \left [ \begin{array}{ccc} a_{3} \\ b_{3} \\ c_{3} \\ \end{array} \right ]$$

~~
 Hi and thanks for Your answer, If I have three vectors in $$\mathbb{R}^2$$ $$\vec{v_1} = \left( \begin{array}{ccc} a_1 \\ a_2\\ \end{array} \right )$$ $$\vec{v_2} = \left( \begin{array}{ccc} b_1 \\ b_2\\ \end{array} \right )$$ $$\vec{v_3} = \left( \begin{array}{ccc} c_1 \\ c_2\\ \end{array} \right )$$ What is the most commen way of proving if these vectors are linear independent or not? I have heard that if the vectors $$\mathrm{span} \{ \mathbb{R}^2 \}$$ then these vectors $$\vec{v_1}, \vec{v_2}, \vec{v_3}$$ are linear independent. Is that correct ??? Sincerely Fred

Recognitions:
 Quote by Mathman23 Hi and thanks for Your answer, If I have three vectors in $$\mathbb{R}^2$$ $$\vec{v_1} = \left( \begin{array}{ccc} a_1 \\ a_2\\ \end{array} \right )$$ $$\vec{v_2} = \left( \begin{array}{ccc} b_1 \\ b_2\\ \end{array} \right )$$ $$\vec{v_3} = \left( \begin{array}{ccc} c_1 \\ c_2\\ \end{array} \right )$$ What is the most commen way of proving if these vectors are linear independent or not? I have heard that if the vectors $$\mathrm{span} \{ \mathbb{R}^2 \}$$ then these vectors $$\vec{v_1}, \vec{v_2}, \vec{v_3}$$ are linear independent. Is that correct ??? Sincerely Fred
Three (3) vectors in $$\displaystyle \mathbb{R}^2$$ are NEVER Linearly Independent.

~~

Sorry,

I'm beginning to get tired.

Hope You can forgive me ??

A set of vectors in $$\mathbb{R}^2$$ are NEVER linear independent because they are situated in the same plane.

Futhermore in my given situation there are three vectors with only two entries each. Therefore these vector are linear dependent.

/Fred

 Quote by xanthym Three (3) vectors in $$\displaystyle \mathbb{R}^2$$ are NEVER Linearly Independent. ~~
 Recognitions: Science Advisor . For Mathman23: *** POP QUIZ *** Given two (2) vectors {V1, V2 ∈ $$\mathbb{R}^{3}$$}, state a necessary and sufficient condition that these vectors are Linearly Independent. CLICK BETWEEN DASHED LINES BELOW To Reveal Answer In Pop-Up Window --------------------- $$\color{white} ANSWER: V1 X V2 NotEqualTo 0$$ --------------------- CLICK BETWEEN DASHED LINES ABOVE To Reveal Answer In Pop-Up Window ~~

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Homework Help
 Quote by Mathman23 Hi and thanks for Your answer, If I have three vectors in $$\mathbb{R}^2$$ $$\vec{v_1} = \left( \begin{array}{ccc} a_1 \\ a_2\\ \end{array} \right )$$ $$\vec{v_2} = \left( \begin{array}{ccc} b_1 \\ b_2\\ \end{array} \right )$$ $$\vec{v_3} = \left( \begin{array}{ccc} c_1 \\ c_2\\ \end{array} \right )$$ What is the most commen way of proving if these vectors are linear independent or not? I have heard that if the vectors $$\mathrm{span} \{ \mathbb{R}^2 \}$$ then these vectors $$\vec{v_1}, \vec{v_2}, \vec{v_3}$$ are linear independent. Is that correct ??? Sincerely Fred
If the number of vetors in your set were equal to the dimensionality of the space, then if the vectors spaned the space they would be linearly independent. You just have too many vectors in your set for this condition to be meaningful.

I'm wondering if you are able to follow some of the other responses that have been given here. It seems to me the notation has been substantially changed around from the problem you originally stated. As I understood your early posts, you were talking about vectors in a function space and had chosen as a basis the linearly independent functions x, y, and z with coefficients given by the defining equations

$v_1 = (a_1,a_2,a_3)$ $v_2 = (b_1,b_2,b_3)$ and $v_3 = (c_1,c_2,c_3)$

Mapping the problem into a 3 dimensional Euclidean vector space is certainly a good way to approach the problem, but the vectors

$$\ \ \ \ \vec{V}_{1} \ = \ \left [ \begin{array}{ccc}a_{1} \\b_{1} \\c_{1} \\\end{array}\right ]\ \ \ \ \ \ \vec{V}_{2} \ = \ \left [ \begin{array}{ccc}a_{2} \\b_{2} \\c_{2} \\\end{array}\right ]\ \ \ \ \ \ \vec{V}_{3} \ = \ \left [ \begin{array}{ccc}a_{3} \\b_{3} \\c_{3} \\\end{array}\right ]$$

have redefined the coefficients, and the linear combination

$$\ \ \ \ x\vec{V}_{1} \ + \ y\vec{V}_{2} \ + \ z\vec{V}_{3} \, \ = \ \, \vec{\mathbf{0}}$$

has given new meaning to x, y, and z. They are analogous to the coefficients I wrote earlier as A, B, and C. In the function space spanned by x, y, z, the condition x = y = z = 0 would be forcing your original function vectors to $\vec{\mathbf{0}}$ There is nothing wrong with different representations of vectors in $$\mathbb{R}^3$$ , but remembering back to my first encounter with function spaces, I think I would have been confused by these changes. But that's just me. If you have it sorted out don't worry about it. If it is confusing to you, I'm sure the notation can be sorted out.

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 Quote by OlderDan ... I think I would have been confused by these changes. But that's just me. If you have it sorted out don't worry about it. If it is confusing to you, I'm sure the notation can be sorted out.
O-Dan:

You're certainly correct that there might be some confusion regarding notation in this thread. Nevertheless, the objective of Msg #9 (and subsequently of Msg #11) was to present this concept:

$$5: \color{red}\ \ \ \ \ \ \left | \begin{array}{ccc} a_{1} & a_{2} & a_{3} \\ b_{1} & b_{2} & b_{3} \\ c_{1} & c_{2} & c_{3} \\ \end{array} \right | \ \ \neq \ \ \textsf{0} \ \ \ \color{black} \Longleftrightarrow \ \ \ \color{red} \{\vec{V}_{1}, \ \vec{V}_{2}, \ \vec{V}_{3} \ \ \ \textsf{Linearly Independent} \}$$

where:

$$1: \ \ \ \ \vec{V}_{1} \ = \ \left [ \begin{array}{ccc} a_{1} \\ b_{1} \\ c_{1} \\ \end{array} \right ] \ \ \ \ \ \ \vec{V}_{2} \ = \ \left [ \begin{array}{ccc} a_{2} \\ b_{2} \\ c_{2} \\ \end{array} \right ] \ \ \ \ \ \ \vec{V}_{3} \ = \ \left [ \begin{array}{ccc} a_{3} \\ b_{3} \\ c_{3} \\ \end{array} \right ]$$

The above stands on its own and should be fairly clear.
(Above Eq #'s reference original #'s in Msg #9.)

~~