Zero eigenvalues or eigenvectors

In summary, there are two sides to the issue of zero eigenvectors and eigenvalues. On one hand, they are allowed and necessary in certain cases, but on the other hand, they can make the concept of an eigenvector trivial. However, zero eigenvalues are not special and can be shifted to create a new matrix. There is also some ambiguity in the definition of an eigenvector, either as a vector satisfying Av= \lambda v or as a non-zero vector with Av= \lambda v.
  • #1
nomadreid
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I have a bit of problem with zero eigenvectors and zero eigenvalues. On one hand, there seems to be nothing in the definition that forbids them, and they even seem necessary to allow because an eigenvalue can serve as a measurement and zero can be a measurement, and if there is a zero eigenvalue then it will be a term in a diagonalized matrix, so that one has a zero eigenvector as well (a column vector of the diagonal matrix with the zero eigenvalue). So far, so good. But on the other hand, if the zero eigenvector is allowed, then every value in the field would be an eigenvalue, hence making it a bit trivial, no?
 
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  • #2
Eigenvectors are non-zero by definition.

An eigenvalue of zero on the other hand is fine. If you have a zero column in your diagonal matrix, you have to chose a non-zero value for the entry which gets multiplied by the zero eigenvalue in order to get a proper eigenvector.
 
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  • #3
I have never seen a zero eigenvector, but zero eigenvectors come up in the theory of vibrations often enough. In that context, it means that the system is positive semi-definite, rather than positive definite. In physical terms, it means that the stiffness matrix is singular and that rigid body motion is possible.
 
  • #4
Thanks, kith and Dr. D.
kith: that clears it up nicely, thanks.
Dr. D. Good to know: except I presume you had a typo, in that you meant , instead of
Dr.D said:
but zero eigenvectors come up in the theory of vibrations
that "but zero eigenvalues come up in the theory of vibrations"
 
  • #5
Note that, generally speaking, zero eigenvalues are nothing special. Given a matrix A with an eigenvector x,
$$
A x = \lambda x
$$
I can construct a new matrix ##B = A - \lambda I##, where ##I## is the identity matrix, such that
$$
B x = 0 x
$$
This is called eigenvalue shifting, and is used in numerical methods for eigenequations, in order to speed up convergence.
 
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  • #6
There is a slight ambiguity in the definition of "eigenvector". Most textbooks define "eigenvalue" for a linear operator, A, as a number, [itex]\lambda[/itex] such that there exist a non-zero vector v with [itex]Av= \lambda v[/itex] and then define an "eigenvector" corresponding to eigenvalue [itex]\lambda[/itex] as such a non-zero vector. But some define eigenvalue in that way and then define "eigenvector" as any vector, v, satisfying [itex]Av= \lambda v[/itex]. I prefer that- it allows one to say things like "any multiple of an eigenvector is an eigenvector" without having to say "except 0" and "the set of all eigenvectors corresponding to eigenvalue [itex]\lambda[/itex] form a vector space" without having to say "with the 0 vector added".
 
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  • #7
Nomadreid, yes, you are correct. I was thinking one thing and typing another.
 
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1. What are zero eigenvalues and eigenvectors?

Zero eigenvalues and eigenvectors are special values and vectors that are associated with a given matrix. They are found by solving the characteristic equation of the matrix and represent the scaling factor and direction of the eigenvectors.

2. Why are zero eigenvalues and eigenvectors important?

Zero eigenvalues and eigenvectors are important in many areas of mathematics and science, including linear algebra, differential equations, and quantum mechanics. They can help us understand the behavior and properties of a system or matrix, and can also be used to simplify calculations and solve equations.

3. How can zero eigenvalues and eigenvectors be used in practical applications?

Zero eigenvalues and eigenvectors can be used in a variety of practical applications, such as image and signal processing, data compression, and machine learning. They can help us identify important features and patterns in large datasets, and can also be used to reduce the dimensionality of a system or dataset.

4. Can a matrix have only zero eigenvalues?

Yes, a matrix can have only zero eigenvalues. This is known as a singular matrix, and it means that the matrix is not invertible and has no non-zero eigenvectors. In this case, the matrix is said to have a nullspace, which contains all the zero eigenvectors.

5. How can we determine the number of zero eigenvalues of a matrix?

The number of zero eigenvalues of a matrix can be determined by counting the number of times the characteristic equation of the matrix has a root of zero. This is also known as the algebraic multiplicity of the zero eigenvalue. Additionally, the geometric multiplicity of the zero eigenvalue can be found by counting the linearly independent zero eigenvectors of the matrix.

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