Eigenvalue as a generalization of frequency

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    Eigenvalue Frequency
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SUMMARY

The discussion centers on the relationship between eigenvalues and frequency, highlighting that eigenvalues can be viewed as a generalization of frequency in certain contexts. Specifically, complex eigenvalues represent rotations and expansions in a plane, which correlate with frequency responses. The argument of a complex eigenvalue indicates the frequency of rotation per unit time, while its magnitude represents gain at that frequency. Understanding these concepts is crucial for applying eigenvalue theory in various mathematical and physical contexts.

PREREQUISITES
  • Understanding of eigenvalues and eigenvectors
  • Familiarity with complex numbers and their geometric interpretation
  • Basic knowledge of matrix operations and properties
  • Concept of orthogonal matrices and their significance
NEXT STEPS
  • Explore the relationship between eigenvalues and frequency in signal processing
  • Study the spectral decomposition of matrices, particularly PDP_inverse
  • Learn about coordinate system transformations and their applications in simulations
  • Investigate the role of orthogonal matrices in rotations and scalings
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Mathematicians, physicists, engineers, and students interested in linear algebra, particularly those exploring the applications of eigenvalues in frequency analysis and transformations.

npit
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Hello everyone.
I understand the concept of eigenvalues and eigenvectors, using usually a geometric intuition, that a eigenvectors of a matrix M are stretched by the corresponding eigenvalue, when transformed through M.

My professor said that eigenvalues represent a generalization of the concept of frequency.
I can not recall the context though.
Can someone provide an short explanation and/or some (not too technical) reading material?

Thanks.
 
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Are you allowing complex eigenvalues and eigenvectors? They are rotations & expansions in a plane, which is related to frequency responses. If so, check out .
 
Thanks.
Did so, I don't understand how it's related to frequency though.
Rotations per time would.
 
Rotations per unit time is the right idea. There are so many different contexts that matrix eigenstructures can be used in that it is hard to do more than give a general intuition. If the (complex) eigenvalue multiplication represents rotation in a unit time, then the amount of rotation in that time (the argument of the eigenvalue) does correspond to a frequency. And the magnitude of the eigenvalue corresponds to a gain (per unit time) at that frequency.

PS. I hate to put words in your professor's mouth. You should probably ask him a follow-up question about what he meant.
 
Hey npit,

You might want to consider the spectral decompositon of PDP_inverse in terms of rotations and scalings.

If you have an orthogonal matrix with R*R^t = I [meaning R^t = R_inverse] then you can make sense of a rotation occurring along with a scaling of each axes and then rotating back again.

Co-ordinate system transformations have the same property (like in physical visualization and simulations) and it can help when the P matrices have the PP^t = I property.
 

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