Proving Linear Independence: Fixed t€R with {u,v}CR^2

In summary, the matrix {u,v}CR^2 has linearly independent vectors if and only if the rank of the matrix is zero.
  • #1
atakel
7
0
Let t€R be fixed. Show that {u,v}CR^2 with u=(cost,sint), v=(-sint,cost) is a linearly inpedendent set.
 
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  • #2
My advice to you is to set up vectors u,v as a 2x2 matrix and plug in values for t;

then how do you conclude that a matrix has linearly independent vectors?
 
  • #3
ı don't know ://
 
  • #4
Do you know what "the rank" of a matrix is?
 
  • #5
I uploadded the original question, there is no information about 'the rank'. In addition that I don't know how to solve the others.
 

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  • #6
So you've never heard of the rank of a matrix? Hmm, that makes it a bit more difficult.

Anyway, our u and v are independent iff

[tex] \alpha u+\beta v=0~\Rightarrow~\alpha=\beta=0[/tex]

were alpha and beta are real number.
So, how do you proceed. You assume that there exists alpha and beta such that

[tex]\alpha (\cos t,\sin t)+\beta (-\sin t,\cos t)=(0,0)[/tex]

this will give you a system of two equations and two unknowns (the alpha and beta).
Solve this system. If you find [tex]\alpha=\beta=0[/tex], then our u and v are independent.
 
  • #7
thank u=)
if u have any idea about other questions, can u help me?
I didn't take a linear cource.. Solving these questions is really diffucult for me://
 
  • #8
Yes, Id be happy to help you with these questions. But first you need to show me what you did yourself to try and solve these questions...
 
  • #9
Actually, at first I started to study ''Partial Differential Equations in Action, Salsa'' but my backround is not enough to understand all subjects. Now I turned back and I started studying linear algebra..
For now, I don't have any idea about solving these questions. I have one week to learn all these subjects..
 
  • #10
I'm sorry to say this, but you should first learn linear algebra and then start worrying abou th questions. We cannot help you until you learned some linear algebra :frown:
 
  • #11
ok thank u:(
 
  • #12
But hey, don't despair. If you're stuck on something, you can always ask us :smile:
 
  • #13
=) I am going to finish all subjects asp.. anf then I will turn back with my new questions.. thank u very much=)
 

Related to Proving Linear Independence: Fixed t€R with {u,v}CR^2

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

Linear independence refers to a set of vectors that cannot be expressed as a linear combination of other vectors in the same vector space. In simpler terms, it means that none of the vectors in the set can be created by combining the others using scalar multiplication and addition.

2. How do you determine if a set of vectors is linearly independent?

To determine if a set of vectors is linearly independent, you can use the determinant method or the rank method. The determinant method involves creating a matrix with the vectors as columns and finding its determinant. If the determinant is non-zero, the vectors are linearly independent. The rank method involves creating an augmented matrix with the vectors and reducing it to row-echelon form. If the rank of the matrix is equal to the number of vectors, they are linearly independent.

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

Yes, a set of two vectors can be linearly independent as long as they are not scalar multiples of each other. This means that they cannot lie on the same line or be parallel to each other.

4. How is linear independence related to linear dependence?

Linear independence is the opposite of linear dependence. If a set of vectors is linearly independent, it means that they are not linearly dependent, and vice versa. In other words, if a set of vectors is not linearly independent, it is linearly dependent, and can be expressed as a linear combination of other vectors in the same vector space.

5. Why is linear independence important in linear algebra?

Linear independence is an important concept in linear algebra because it allows us to determine the number of linearly independent equations in a system and find the solutions. It also helps in determining the basis of a vector space and solving problems involving vectors and matrices. Additionally, linear independence is crucial in many applications, such as data analysis, computer graphics, and machine learning.

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