Equivalence of the nullspace and eigenvectors corresponding to zero eigenvalue

In summary, the null space of a square matrix A corresponds to eigenvectors of A with a zero eigenvalue, which can be any nonzero vector v such that Av=0. The geometric multiplicity of this eigenvalue is the dimension of the null space, while the algebraic multiplicity is the power of x that divides the characteristic polynomial. The two can be different, as in cases where the matrix has all zeroes on and below the diagonal but all 1's above it. The null space and the set of eigenvectors with 0 eigenvalue are the same set if we include the 0 vector as an eigenvector, but if we require that eigenvectors be nonzero, then the null space is a superset
  • #1
onako
86
0
Suppose a square matrix A is given. Is it true that the null space of A corresponds to eigenvectors of A being associated with its zero eigenvalue? I'm a bit confused with the terms 'algebraic and geometric multiplicity' of eigenvalues related to the previous statement? How does this affect the statement?
 
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  • #2
yes, at least a non zero vector in the nullspace of T is just a non zero vector v such that Tv = 0.v, hence is an eigenvector for the eigenvalue zero.

the geometric multiplicity of this eigenvalue is the dimension of the null space. and the algebraic multiplicity is the power of x that divides the characteristic polynomial. they can be different, as in the case where the matrix has all zeroes on and below the diagonal but all 1's above it. then the null space has dimension one, the ch poly is X^n and the alg mult is n.
 
  • #3
@mathwonk

Thanks. What is the exact meaning of "at least" in your statement? Could the set of vectors of null vectors be a superset of the set of eigenvectors corresponding to zero eigenvalue (of course, any linear comb of such eigenvectors will also be a vector of null space)?
 
  • #4
onako said:
@mathwonk

Thanks. What is the exact meaning of "at least" in your statement? Could the set of vectors of null vectors be a superset of the set of eigenvectors corresponding to zero eigenvalue (of course, any linear comb of such eigenvectors will also be a vector of null space)?

Aren't the eigenvectors a basis of the null space? That is, if you have a 2D null space then you have two basis vectors, but infinitely many vectors could be in the null space.
 
  • #5
Some texts require that an "eigenvector" of A with eigenvalue [itex]\lambda[/itex] is a nonzero vector, v, such that [itex]Av= \lambda v[/itex] which then requires that we say that "the set of all eigenvectors of A with eigenvalue [itex]\lambda[/itex] together with the 0 vector form a vector space". Other texts will say that "[itex]\lambda[/itex] is an eigenvalue of A if there exists a non-zero vector v such that [itex]Av= \lambda v[/itex] but then say that an eigenvector is any vector v such that [itex]Av= \lambda v[/itex], even the 0 vector.

If you require that an "eigenvector" not be 0, then, yes, any "eigen space" is a "superset" of the set of a "eigenvectors" specifically because it includes 0. However, if you include the 0 vector as an "eigenvector", the two sets, the null space and the set of eigenvectors with 0 eigenvalue are exactly the same.
 

1. What is the significance of the equivalence between nullspace and eigenvectors corresponding to zero eigenvalue?

The equivalence between nullspace and eigenvectors corresponding to zero eigenvalue is important because it provides a deeper understanding of the relationship between these two concepts in linear algebra. It shows that the nullspace of a matrix, which represents the set of all vectors that are mapped to the zero vector by the matrix, is closely related to the eigenvectors whose corresponding eigenvalues are equal to zero.

2. How is the nullspace related to the eigenvectors corresponding to zero eigenvalue?

The nullspace and the eigenvectors corresponding to zero eigenvalue are mathematically equivalent. This means that any vector in the nullspace of a matrix can also be considered an eigenvector with a corresponding eigenvalue of zero. Similarly, any eigenvector with a corresponding eigenvalue of zero can be considered a member of the nullspace of the matrix.

3. Can the nullspace and eigenvectors corresponding to zero eigenvalue be used interchangeably?

Although the nullspace and eigenvectors corresponding to zero eigenvalue are equivalent, they cannot be used interchangeably. This is because the nullspace is a set of vectors, while eigenvectors are individual vectors. Additionally, the nullspace is a property of a matrix, while eigenvectors are associated with a specific eigenvalue.

4. How does the equivalence between nullspace and eigenvectors corresponding to zero eigenvalue relate to the dimension of the nullspace?

The dimension of the nullspace of a matrix is equal to the number of linearly independent eigenvectors corresponding to zero eigenvalue. This is because the nullspace contains all vectors that are mapped to the zero vector by the matrix, and these vectors are precisely the eigenvectors with a corresponding eigenvalue of zero.

5. Can the nullspace and eigenvectors corresponding to zero eigenvalue provide insights into the structure of a matrix?

Yes, the nullspace and eigenvectors corresponding to zero eigenvalue can provide insights into the structure of a matrix. For example, if a matrix has a large nullspace, it may be an indication that the matrix is not invertible. Additionally, the eigenvectors corresponding to zero eigenvalue can provide information about the symmetry and diagonalizability of a matrix.

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