Eigenvectors and the dot product

Then use the fact that <v1,v2> = <Av1,v2> to conclude that <v1,v2> = 0 when \lambda1 not equal to \lambda2.In summary, we discussed the properties of a symmetric matrix A and how it relates to its eigenvalues and eigenvectors. We showed that for any symmetric matrix, there are always real eigenvalues and that the eigenvectors corresponding to different eigenvalues are always orthogonal. This was proven by comparing the dot product of two eigenvectors and using the fact that A is symmetric.
  • #1
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Homework Statement



Suppose the the matrix A is symmetric, meaning that
A =

a b
b d

Show that for any symmetric matrix A there are always real eigenvalues. Also, show that
the eigenvectors corresponding to two di erent eigenvalues are always orthogonal; that is,
if V1 and V2 are the eigenvectors for eigenvalues [tex]\lambda[/tex]1 and [tex]\lambda[/tex]2, with [tex]\lambda[/tex]1 not equal to [tex]\lambda[/tex]2, then the dot
product V1 * V2 = 0.
HINT: Compare [tex]\lambda[/tex]1V1*V2 to V1*[tex]\lambda[/tex]2V2 .


Homework Equations



-

The Attempt at a Solution



I was able to show that any eigenvalue from this matrix would always be positive. That wasn't too bad. Its the second part that is tripping me up. I don't get how the hint is really all that helpful. I want to find values of V1 and V2 don't I? I'm confused.
 
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  • #2
You don't need to find the eigenvectors explicitly. All you need to show is that their dot product is zero.

Consider <Av1,v2> and <v1,Av2> and, using the fact that A is symmetric, show that they are equal.
 

Related to Eigenvectors and the dot product

1. What is an eigenvector?

An eigenvector is a vector that, when multiplied by a specific matrix, results in a scalar multiple of itself. In other words, the direction of the eigenvector remains unchanged, but its magnitude is multiplied by a constant value. Eigenvectors are an important concept in linear algebra and are used in various applications, such as image processing and data analysis.

2. What is the dot product?

The dot product, also known as the scalar product, is a mathematical operation that takes two vectors as input and produces a scalar as output. It is calculated by multiplying the corresponding components of the two vectors and then summing the results. The dot product is used to determine the angle between two vectors, the projection of one vector onto another, and the magnitude of a vector.

3. How are eigenvectors and the dot product related?

Eigenvectors and the dot product are related in that the dot product can be used to find the eigenvalues of a matrix. The eigenvalues of a matrix are the scalar values that, when multiplied by the corresponding eigenvectors, result in the original matrix. The dot product is also used in the process of finding eigenvectors, as it is used to determine the direction of the eigenvector in relation to the matrix.

4. What are some applications of eigenvectors and the dot product?

Eigenvectors and the dot product have numerous applications in various fields, including physics, engineering, and computer science. In physics, they are used to study the behavior of quantum particles. In engineering, they are used in structural analysis and control systems. In computer science, they are used in data compression and machine learning algorithms.

5. Can eigenvectors and the dot product be extended to complex numbers?

Yes, eigenvectors and the dot product can be extended to complex numbers. Complex eigenvalues and eigenvectors are used in quantum mechanics and other areas of physics. The dot product of two complex vectors is calculated by taking the complex conjugate of one vector and multiplying it by the other vector, similar to how it is calculated with real numbers.

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