Eigenvectors and Row/Column Vectors: What's the Connection?

In summary, eigenvectors are vectors that do not change direction when multiplied by a matrix and are important in many areas of mathematics and science. They are closely related to eigenvalues, which determine their scaling factor. To find eigenvectors, one must first find the eigenvalues and then solve an equation. Real-world applications of eigenvectors include image and signal processing, data compression, and machine learning algorithms.
  • #1
Mappe
30
0
Is there a general between the eigenvectors of a matrix and the row (or column) vectors making up the matrix?
 
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  • #2
General relation I meant off cause ;\
 
  • #3
Mappe said:
Is there a general between the eigenvectors of a matrix and the row (or column) vectors making up the matrix?
Not that I'm aware of.
Mappe said:
General relation I meant off cause ;\
Do you mean, "of course"?
 

1. What is an eigenvector?

An eigenvector is a vector that does not change its direction when multiplied by a specific matrix. It only changes in magnitude by a scalar factor known as the eigenvalue.

2. What is the importance of eigenvectors?

Eigenvectors are important in many areas of mathematics and science, particularly in linear algebra and quantum mechanics. They are used to find solutions to systems of linear equations and to understand the behavior of dynamical systems.

3. How are eigenvectors and eigenvalues related?

Eigenvectors and eigenvalues are closely related. Eigenvectors are associated with specific eigenvalues, and the eigenvalues determine the magnitude by which the eigenvectors are scaled when multiplied by a matrix.

4. How can I find eigenvectors?

To find eigenvectors, you need to first find the eigenvalues of a matrix. This can be done by solving the characteristic equation of the matrix. Once the eigenvalues are known, the corresponding eigenvectors can be found by solving the equation (A - λI)x = 0, where A is the matrix and λ is the eigenvalue.

5. What are some real-world applications of eigenvectors?

Eigenvectors have many practical applications. They are used in image and signal processing, data compression, and in the analysis of social networks. They are also used in machine learning algorithms, such as principal component analysis (PCA), for dimensionality reduction and feature extraction.

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