Linear Algebra Eigenvector Properties

In summary, the conversation discusses whether or not the statement "If A and B have the same eigenvector X, then A+B should also have the same eigenvector, X" is true or false. It is determined to be true and a proof is provided. The conversation also touches on the question's wording and clarifies the correct interpretation.
  • #1
FinalStand
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0

Homework Statement



True/False: If true give a proof, if false give a counterexample.
a)
If A and B have the same eigenvector X, then A+B should also have the same eigenvector, X.
b)
if A has an eigenvalue of 2, and B has an eigenvalue of 5, then 7 is an eigenvalue of A+B




Homework Equations





The Attempt at a Solution



for b):
2 0 2 3
0 2 has eigenvalue of 2; 3 2 has eigenvalue of 5

When I add them together (A+B) you get 4 3
3 4

Then I found an eigenvalue of 7; Is this correct?
Or the property of A+B != eigenvalueA + eigenvalueB is always correct? But this question's wording is kind of weird, because it said if its true give a counterexample ...


for a) I think it is false,...not entirely sure though.
 
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  • #2
FinalStand said:
True/False: If true give a proof, if false give a counterexample.



But this question's wording is kind of weird, because it said if its true give a counterexample ...

That's not what I read.
 
  • #3
Ok I am stupid, I read the question wrong so I confused myself...here goes my mark...
 
  • #4
(a) asks you to show that If X is an eigenvector for both A and B then it is an eigenvector for A+ B. If X is an eigevector of A, then [itex]AX= \lambda_A X[/itex] for some number [itex]\lambda_A[/itex]. If X is an eigenvector of B, then [itex]BX= \lambda_B X[/itex] for some number [itex]\lambda_B[/itex]. Now, what can you say about (A+ B)X?
 

What is a linear algebra eigenvector?

A linear algebra eigenvector is a vector that, when multiplied by a square matrix, results in a scalar multiple of itself. In other words, the direction of the vector remains unchanged, but its magnitude is scaled by a constant factor.

What are the properties of eigenvectors?

There are several properties of eigenvectors, including:

  • Eigenvectors are non-zero vectors.
  • Eigenvectors are orthogonal to each other if they correspond to different eigenvalues.
  • Eigenvectors are not unique - there can be multiple eigenvectors for the same eigenvalue.
  • The sum of the eigenvalues is equal to the trace of the matrix.
  • The product of the eigenvalues is equal to the determinant of the matrix.

How are eigenvectors and eigenvalues related?

Eigenvectors and eigenvalues are closely related. Eigenvectors are the vectors that, when multiplied by a matrix, result in a scaled version of themselves. The corresponding eigenvalue is the scalar multiple by which the eigenvector is scaled. Each eigenvector has one unique eigenvalue.

What are the applications of eigenvectors and eigenvalues?

Eigenvectors and eigenvalues have many applications in various fields, including:

  • Principal component analysis in statistics and data analysis.
  • Image and signal compression in computer science.
  • Stability analysis in physics and engineering.
  • Markov chain analysis in probability and statistics.
  • PageRank algorithm in web search engines.

How can I find eigenvectors and eigenvalues?

The process of finding eigenvectors and eigenvalues involves solving a system of linear equations. This can be done using various methods, such as the characteristic polynomial, the power iteration method, or the QR algorithm. There are also many software tools and calculators available that can find eigenvectors and eigenvalues for a given matrix.

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