Linearly Independent Eigen Vectors

In summary, the student had initially made an error in calculating the Eigen Values but has now corrected it and gotten the correct values of +3 and -3. They also found the corresponding Eigen Vectors of [+1, -1] for +3 and [8, 2] for -3. The book's answer is B, but the student is unsure of how to arrive at that answer and has asked for clarification. They also mention struggling with understanding a question about a hermition matrix. Finally, the student confirms that their initial question has been resolved and agrees with the advice to start a new thread for their other question.
  • #1
jaus tail
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Solved (sorry i tried again and realized my E-values were wrong)
1. Homework Statement

upload_2018-1-30_14-46-49.png


Homework Equations


Find Eigen Values and then what?

The Attempt at a Solution


I got eigen values as 3 and -3.
Now how to proceed?
I got Eigen Vector as: 1, 1 for eigen value of 3
and eigen vector as 8, 2 for eigen value of -3
so these are 2 independent eigen vector.
Book answer is B.
How?
 

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  • #2
Show your work, not just the results, please
 
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  • #3
Okay. Will be careful for next time.
I'm struggling with this question. Thought of posting here instead of in other thread.
upload_2018-1-30_17-7-35.png

I think it's hermition matrix as A = (transpose and then conjugate of A)
But book says answer is D.
 

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  • #4
jaus tail said:
Thought of posting here instead of in other thread
Better to start a new thread: you get more help that way, too !

Post #1 resolved ?
 
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  • #5
Yes post#1 is solved. I had calculated wrong Eigen Values. Eigen values are +3, and +3. For +3 Eigen Vectors are [+1, -1] and there is no other combination of Eigen Vector that isn't a linear multiple of this vector. So there's only 1 Eigen Vector.
 
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  • #6
For post #3 I agree with you.
 
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  • #7
Thanks.
 

What is the definition of "Linearly Independent Eigen Vectors"?

Linearly Independent Eigen Vectors are a set of vectors that are not dependent on each other and can be used to represent a linear transformation of a matrix.

Why are Linearly Independent Eigen Vectors important in linear algebra?

Linearly Independent Eigen Vectors are important because they provide a basis for a vector space and allow for the representation of a linear transformation in a more efficient and simplified manner. They also have a variety of applications in fields such as physics, engineering, and computer science.

How can you determine if a set of vectors are linearly independent eigen vectors?

To determine if a set of vectors are linearly independent eigen vectors, you can use the determinant or rank of the matrix formed by these vectors. If the determinant is non-zero or the rank is equal to the number of vectors, then the set is linearly independent.

Is it possible for a set of linearly dependent vectors to also be eigen vectors?

No, a set of linearly dependent vectors cannot be eigen vectors. This is because eigen vectors must be linearly independent in order to form a basis for the vector space.

Can a matrix have an infinite number of linearly independent eigen vectors?

No, a matrix can have at most n linearly independent eigen vectors, where n is the dimension of the matrix. This is because the maximum number of linearly independent vectors in a vector space is equal to its dimension.

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