Linear Algebra Question: Eigenvectors & Eignvalues

In summary, the conversation discusses the last homework assignment in Linear Algebra, where the student is tasked with making conjectures about the eigenvectors and eigenvalues of a matrix A corresponding to given transformations. Two specific transformations are mentioned, reflection about the line y=x and contraction by a factor of 1/2. The student is unsure of how to get started but gives some initial thoughts on the possible eigenvalues based on the geometric properties of the transformations. It is also mentioned to consider specific cases to confirm the conjectures with computations.
  • #1
FrogginTeach
13
0
This is my last week in Linear Algebra. I am working on our last homework assignment before the exam so I want to make sure I know what I am doing.

In each part, make a conjecture about the eigenvectors and eigenvalues of the matrix A corresponding to the given transformation by considering the geometric properties of multiplication by A. Confirm each of your conjectures with computations.

A. Reflection about the line y=x
B. Contraction by a factor of 1/2

I'm not quite sure how to get started.
 

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  • #2
reflection about a line does not change the length of a vector. That sharply restricts the possible eigenvalues. Also consider two crucial cases:
(1) reflection about y= x of the vector <1, 1>, lying in y= x.
(2) reflection about y= x of the vector <1, -1>, perpendicular to y= x.

For the second, think about happens to <x, y>. "Contraction by a factor of 1/2" changes <x, y> to what vector? How can that be equal to [itex]\lambda <x, y>[/itex]?
 

1. What is an eigenvector and eigenvalue?

An eigenvector is a vector that remains in the same direction after a linear transformation. An eigenvalue is a scalar that represents the amount by which the eigenvector is stretched or compressed during the transformation.

2. Why are eigenvectors and eigenvalues important in linear algebra?

Eigenvectors and eigenvalues are important because they provide a way to understand and analyze linear transformations, which are essential in many fields of science and engineering. They also have many applications in data analysis and machine learning.

3. How are eigenvectors and eigenvalues calculated?

Eigenvectors and eigenvalues can be calculated by finding the solutions to a specific equation known as the characteristic equation. This equation involves the transformation matrix and the scalar value that represents the eigenvalue.

4. What is the relationship between eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are closely related, as each eigenvector has a corresponding eigenvalue. The eigenvalue determines the magnitude of the eigenvector's stretch or compression during a transformation.

5. Can there be multiple eigenvectors for one eigenvalue?

Yes, there can be multiple eigenvectors for one eigenvalue. In fact, there can be an infinite number of eigenvectors for a single eigenvalue, as long as they are all linearly independent.

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