Prove Linear Independence of x1 and x2

In summary, x1 and x2 are shown to be linearly independent by setting their respective equations equal to each other and creating a linear combination that eliminates x3. This is equivalent to the definition of linear independence where one vector is a constant multiple of the other. The given vectors for the problem may be incorrect as x1 and x2 are actually linearly independent, not linearly dependent.
  • #1
s_stylie0728
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Homework Statement


x1, x2, and x3 are linearly dependent. Show that x1 and x2 are linearly independent.


Homework Equations


After reduction using gaussian elimination, x1, x2, and x3 are proven to be linearly dependent because x1 and x2 are defined by x3 (being the free variable) as:

x1-x2-6x3 = 0
x2-2x3 = 0


The Attempt at a Solution


I set the two above equations equal to each other and created a linear combination of x1 and x2 to create x3. This came out to be:

(1/4)x1 - (1/2)x2 = x3

I did this because according to theory, I should define x3 as a linear combination of the two I'm trying to prove to be linearly independent because this eliminates x3. But my vector space is R^3, so I'm confused on how to "eliminate" x3. In order to prove linear independence the vectors must be equal to 0. But in this case, their equal to x3. I can't just say (1/4)x1 - (1/2)x2 = 0 can I? Then I tried solving for (1/4)x1 - (1/2)x2 - x3 = 0, and I just got definitions in terms of free variables again. My book gives no examples, so I just have my elementary grasp read from a complex theory to work off of here. Any ideas? I would appreciate it!
 
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  • #2
Im a bit confused. Were you given three specific vectors to work with, or are you working with three arbitary vectors?
 
  • #3
Ha, yeah. Sorry...

x1 = {2,1,3}
x2 = {3,-1,4}
x3 = {2,6,4}
 
  • #4
A set of vectors {v1, v2, ..., vn} is linearly dependent iff the equation c1*v1 + c2*v2 + ... + cn*vn=0 has a solution where at least one of the constants ci is not zero.

If you're checking two vectors, the definition above can be used, but you can do something simpler (that is equivalent to this definition): one vector will be a constant multiple of the other.

Have you given us the right vectors for the problem you posted? x1 and x2 are linearly independent, not linearly dependent.
 

Related to Prove Linear Independence of x1 and x2

1. What does it mean for two vectors to be linearly independent?

Two vectors x1 and x2 are linearly independent if there is no scalar c such that c*x1 = x2. In other words, they are not multiples of each other and cannot be written as a linear combination of each other.

2. How do you prove that x1 and x2 are linearly independent?

A common method to prove linear independence is by using the definition and showing that there is no scalar c that satisfies c*x1 = x2. Another method is by using the determinant of a matrix. If det([x1, x2]) is not equal to 0, then x1 and x2 are linearly independent.

3. Can two vectors that are not multiples of each other still be linearly dependent?

Yes, two vectors can still be linearly dependent if they are not multiples of each other. This means that there exists a linear combination of the two vectors that equals the zero vector. For example, if x1 = [1, 2] and x2 = [2, 4], they are not multiples of each other but 2*x1 - x2 = [0, 0].

4. Are there any other methods to prove linear independence besides the determinant of a matrix?

Yes, there are other methods such as using Gaussian elimination to reduce the vectors to row-echelon form and checking if there are any rows of all zeroes. Another method is by using the Gram-Schmidt process to orthogonalize the vectors and seeing if they are still linearly independent after the process.

5. What is the importance of proving linear independence of two vectors?

Proving linear independence is important in linear algebra because it helps determine if a set of vectors is a basis for a vector space. It also helps in solving systems of linear equations, as linearly independent vectors can be used to represent solutions to the system.

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