Linear algebra, eigenvectors and eigenvalues

In summary, if v is an eigenvector of an invertible matrix A, then it is also an eigenvector of 2A and A^2, but not necessarily of A^-1.
  • #1
ann.r221
1
0
If v is an eigenvector of an invertible matrix A, which of the following is/are necessarily true?

(1) v is also an eigenvector of 2A
(2) v is also an eigenvector of A^2
(3) v is also an eigenvector of A^-1

A) 1 only
B) 2 only
C) 3 only
D) 1 and 3 only
E) 1,2 and 3

I am pretty sure 2 is true because if we look at Av = (lamba)v.
A^2v = A(lambda)v = (Av)(lambda) = (lambda)v(lambda) = (lambda)^2 v

So A^2v = (lambda)^2v. So that should be that v is an eigenvector for A^2 as well. I am not sure about the others can someone help?
 
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  • #2
You did so well on b), isn't a) just as easy? (2A)v=? For c) use A^(-1)A=I.
 

Related to Linear algebra, eigenvectors and eigenvalues

1. What is linear algebra?

Linear algebra is a branch of mathematics that deals with the study of linear equations and their representations in vector spaces. It involves the use of matrices, vectors, and other mathematical structures to solve problems related to systems of linear equations.

2. What are eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are two important concepts in linear algebra. An eigenvector is a vector that does not change direction when multiplied by a particular matrix. An eigenvalue is a scalar value that represents the amount by which the eigenvector is stretched or compressed when multiplied by the matrix.

3. How are eigenvectors and eigenvalues useful?

Eigenvectors and eigenvalues are useful in many fields, such as physics, engineering, and computer science. They can be used to simplify complex systems and make calculations more efficient. In physics, they are used to find the direction and magnitude of motion in a system. In engineering, they are used to analyze structures and solve optimization problems. In computer science, they are used in machine learning algorithms and image processing techniques.

4. How do you find eigenvectors and eigenvalues?

To find eigenvectors and eigenvalues, you need to solve the characteristic equation of a matrix. This involves finding the determinant of the matrix and solving for the roots of the characteristic polynomial. Once you have the eigenvalues, you can find the corresponding eigenvectors by solving a system of linear equations.

5. What are some real-world applications of linear algebra, eigenvectors, and eigenvalues?

Linear algebra, eigenvectors, and eigenvalues have numerous real-world applications. They are used in image and signal processing, data compression, computer graphics, and robotics. In finance, they are used to analyze stock market data and make predictions. In machine learning, they are used to classify data and make predictions based on patterns and trends. They also have applications in chemistry, biology, and social sciences.

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