What does the Eigenvalue of a linear system actually tell you?

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Discussion Overview

The discussion revolves around the significance of eigenvalues in linear systems, exploring their implications in various contexts such as geometry, physics, and mathematical properties. Participants seek to clarify what eigenvalues reveal about the behavior and characteristics of these systems.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant expresses confusion about the practical importance of eigenvalues in linear systems and requests a straightforward explanation.
  • Another participant suggests that having independent eigenvectors with an eigenvalue of 1 indicates that the matrix is the identity matrix, while different eigenvalues can indicate geometric transformations such as reflections.
  • It is proposed that in the context of forces or torques, eigenvalues represent effective mass or moment of inertia in specific modes, while for derivatives, they relate to time constants.
  • Further clarification is provided that the system will not remain in a mode other than the eigenvectors, implying that the mathematical model remains valid but the behavior changes if the system starts in a different mode.
  • A participant discusses the relationship between the trace and determinant of a matrix and its eigenvalues, noting conditions for invertibility and diagonalizability.
  • Another viewpoint describes eigenvectors as vectors that are only stretched or contracted by the matrix, with eigenvalues indicating the degree of this transformation.

Areas of Agreement / Disagreement

Participants present multiple perspectives on the significance of eigenvalues, with no consensus reached on a singular interpretation or application. The discussion remains unresolved regarding the broader implications of eigenvalues in different contexts.

Contextual Notes

Some participants mention specific conditions under which eigenvalues and eigenvectors apply, such as the necessity of starting in eigenvector modes for certain interpretations to hold. There are also references to mathematical properties that may depend on the nature of the matrix involved.

newclearwintr
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I know that the eigenvalue of a linear system is a scalar such that Ax=λx. I know many ways to find the eigenvalue of a linear system. But I'm pulling my hair out trying to figure out what it is actually telling me about the system.

Can anyone give me a non-technical straight up answer on why eigenvalues are important and what that information provides us in regards to a linear system?

Thanks!
 
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well if you had n independent eigenvectors all with eigenvalue 1, then A = Id.

If you have an orthogonal matrix in dim n= 2, and one eigenvalue is 1 and one eigenvalue is -1, you have a reflection.

... it helps describe the geometry of the map.
 
welcome to pf!

hi newclearwintr! welcome to pf! :smile:

if A is a force or torque, then the eigenvalue is the effective mass or effective moment of inertia in a particular mode

if A is a derivative, then the eigenvalue is the time constant in a particular mode

(the only modes that will work are the eigenvectors …

if the system starts in any other mode, it won't stay in it, so the concept of effective mass or whatever is inapplicable)
 


tiny-tim said:
hi newclearwintr! welcome to pf! :smile:

if A is a force or torque, then the eigenvalue is the effective mass or effective moment of inertia in a particular mode

if A is a derivative, then the eigenvalue is the time constant in a particular mode

(the only modes that will work are the eigenvectors …

if the system starts in any other mode, it won't stay in it, so the concept of effective mass or whatever is inapplicable)

Thanks for your response tiny-tim! So when you say it won't stay in the mode, we are saying that the mathematical model used to describe whatever phenomenon we're talking about will no longer be applicable? That constant is what allows the system to work (ie stay in the mode)?
 
hi newclearwintr! :smile:

no, the mathematical model is fine …

suppose the equation is L = Iω (angular momentum = moment of inertia times angular velocity)

the I here is a tensor, not a number, and L is generally not parallel to ω

only if ω is parallel to a principal axis of the body, is L parallel to ω

ie the only modes with L parallel to ω are rotations about the principal axes (the eigenvectors)

for any other mode, even if angular momentum is constant (conserved), the angular velocity won't be … the rotation won't stay in the mode it started in!
 
The trace of matrix A which equals the sum of its diagonal elements equals the sum of all eigenvalues of the matrix and the determinant of A equals the product of the eigenvalues. A is invertible if and only if all the eigenvalues are nonzero. The solutions of the characteristic polynomial of A are exactly its eigenvalues while a real matrix B is diagonalizable if all its eigenvalues are real and distinct. A symmetric matrix can be diagonalized in case its eigenvalues are distinct, eigenvalues of Hermitian matrices are real, and eigenvalues of skew-symmetric matrices are purely imaginary or zero. When A is invertible the eigenvalues of A^-1 are precisely the multiplicative inverses of the eigenvalues of A.

Planetmath: eigenvalues (of a matrix)
Wolfram Mathworld: Eigenvalue
Wikipedia: Eigenvalues
 
I guess one way to think about eigenvectors is that they are vectors that the matrix only stretches or contracts. If the eigenvalue associated to an eigenvector is less than one, the matrix contracts the vector (makes it shorter) and if the eigenvalue is bigger than 1, the matrix stretches the vector. Of course, if the eigenvalue is 1, the matrix is just the identity on that vector.
 

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