Diagonalizability of Singular Matrices: Investigating Rank and Eigenvectors

  • Thread starter Thread starter Dank2
  • Start date Start date
  • Tags Tags
    Matrix
Click For Summary

Homework Help Overview

The discussion revolves around the diagonalizability of a 3x3 singular matrix A, given certain conditions related to its rank and the matrix A + 5I. Participants explore the implications of A being singular and the relationship between the ranks of A and A + 5I, as well as the eigenvalues and eigenvectors associated with these matrices.

Discussion Character

  • Exploratory, Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants discuss the meaning of singularity and its implications for the ranks of A and A + 5I. Questions arise about the relationship between eigenvalues, eigenvectors, and diagonalizability. Some participants attempt to clarify the conditions under which a matrix can be diagonalizable and the significance of geometric versus algebraic multiplicities of eigenvalues.

Discussion Status

The discussion is ongoing, with participants providing insights and questioning assumptions about the properties of the matrices involved. Some guidance has been offered regarding the relationship between eigenvalues and diagonalizability, but no consensus has been reached on the final determination of A's diagonalizability.

Contextual Notes

Participants note that A is singular, which implies its rank is less than 3. There is also mention of the Jordan normal form and the conditions under which a matrix can be diagonalized, highlighting the complexity of the discussion regarding eigenvalues and their multiplicities.

Dank2
Messages
213
Reaction score
4

Homework Statement


Let A be a 3x3 singular Matrix that satisfy:
p(A+5I) < p(A)
p - is the rank of the matrix
I - is the identity matrix,
Is A Diagonalizable?

Homework Equations

The Attempt at a Solution


I know that A diagonalizable matrix can be Singular from every rank, even at 0 rank, so i can't see how i can conclude anything.
 
Last edited:
Physics news on Phys.org
You can exclude many cases by the given assumptions. What does it mean for A to be singular?
Can A or A+5I equal the zero matrix?
 
Last edited:
  • Like
Likes   Reactions: Dank2
singular means it isn't invertible or the columns are linearly independent or the rank is lower than 3.
i think A + 5I cannot be the zero matrix, but how's that even related to the Diagnolization of A, since any rank of matrix can be diagonliazable.
 
There is only one possibility for the ranks of A and A + 5I. Assume that A is diagonalizable. What does it mean for the eigenvalues? This gives you a criterium.
 
  • Like
Likes   Reactions: Dank2
You mean the eigenvectors? they are independent. if not i don't know, i know that if A is diagonalizable it means that the amount of the same eigenvalues should be equal to the dimension of the eigenvectors space that is related to the current eigenvalue.

also the rank of A<3
and i can't see how to prove that A+5I > 0 it does look reasonable to me.
So A+5I > 0 ==> A+5I = > 1
and then rank of A = 2.
How is that helps me determine if A Diagonalizable
 
Still need help
 
I meant the eigenvalues.
##p(A)=2## and ##p(A+5I)=1## are the only possibilities for the ranks, since ##p(A) < 3## and neither matrix can be zero which is equivalent to rank zero. (If ##A+5I = 0## then ##A = -5I## of rank ##3##. If ##A=0## then there is no way for another rank below that.)

Now assume ##A## is diagonalizable, i.e. there is a matrix ##S## with ##SAS^{-1} = diag(α,β,γ)##.
Then ##S(A+5I)S^{-1} = SAS^{-1} + 5I = diag(α+5,β+5,γ+5)## and without loss of generality ##γ=0## because ##p(A)=2.##
Now we still have ##p(A+5I)=1## which is only possible for ##α = β = -5##.
 
  • Like
Likes   Reactions: Dank2
So if the rank of the matix nxn is k , then the amount of 0 eigenvalues is n-k ? .
and also if i get two eigenvalues that are 0, how can i know if the matrix is diagonal or not ?
 
Dank2 said:
You mean the eigenvectors? they are independent. if not i don't know, i know that if A is diagonalizable it means that the amount of the same eigenvalues should be equal to the dimension of the eigenvectors space that is related to the current eigenvalue.

also the rank of A<3
and i can't see how to prove that A+5I > 0 it does look reasonable to me.
So A+5I > 0 ==> A+5I = > 1
and then rank of A = 2.
How is that helps me determine if A Diagonalizable

Why do you say p(A) < 3? All you were told is that p(A+5I) < p(A), and so you can have p(A) = 3, p(A+5I) = 1 or 2.
 
  • #10
Ray Vickson said:
Why do you say p(A) < 3? All you were told is that p(A+5I) < p(A), and so you can have p(A) = 3, p(A+5I) = 1 or 2.
Because A is singular, that means it has linearly independent columns, and that means the rank must be less than 3.
 
  • #11
I think I've got it, the rank of P'AP = Rank of A .
Then Rank of A = Rank of it's Diagonal matrix . therefore the rank - n is equal to the number of 0 on the diagonal.
But, still haven't figured out if it's Diagonalizable or not.
 
  • #12
Dank2 said:
So if the rank of the matix nxn is k , then the amount of 0 eigenvalues is n-k ? .
Amount isn't a good word here. In case of an eigenvalue ##\lambda## we speak of geometric multiplicity (dimension of the eigenspace of ##\lambda##) and algebraic multiplicity (power of ##t-\lambda## in the characteristic polynomial ##det (A-tI) = 0##) . If ##A## is diagonalizable then they are equal.
In general (in the situation asked) ##n = p(A) + \dim \ker(A) = k + (n-k)## and ## \ker(A) = \{x \in V | Ax=0=0 \cdot x\}## the eigenspace of eigenvalue ##0##.

and also if i get two eigenvalues that are 0, how can i know if the matrix is diagonal or not ?
You can't. It heavily depends on the scalar domain that make up your coordinates.
 
  • Like
Likes   Reactions: Dank2
  • #13
fresh_42 said:
You can't. It heavily depends on the scalar domain that make up your coordinates.
Or maybe A is diagonalizable since the rank of A is 2, and ker of A=1, and it does have only one eigenvalue = 0 , and so the dim of the eigenspace of 0 is 1 .

How can i see the the dim of the Eigenspace of -5 is 2 ?
 
Last edited:
  • #14
Dank2 said:
The question in my book says if A is Diagonalizable? Maybe they have mistake ?
If ##A = diag(-5,-5,0)## then ##A## is diagonal and all conditions are met. We only derived that there can't be other eigenvalues.
 
  • Like
Likes   Reactions: Dank2
  • #15
fresh_42 said:
If ##A = diag(-5,-5,0)## then ##A## is diagonal and all conditions are met. We only derived that there can't be other eigenvalues.
Thanks, think i got it.
 
Last edited:
  • #16
fresh_42 said:
If ##A = diag(-5,-5,0)## then ##A## is diagonal and all conditions are met. We only derived that there can't be other eigenvalues.
I wondered one more thing, if i could show that the dim of the eigenspace of -5 is 2 ? or do i must have values of the matrix for that ?
 
  • #17
Dank2 said:
Thanks, think i got it.
If
A = \pmatrix{-5 &amp; 1 &amp; 0 \\ 0 &amp; -5 &amp; 0 \\ 0 &amp; 0 &amp; 0}
then all the conditions are met and ##A## cannot be diagonalized.
 
  • #18
You mean can be ? if not :
I = Identity matrix
I' = Inverse
I'AI = A
which means A is diagonlaziable. We just found matrix I that Diagonalize A.
 
Last edited:
  • #19
Dank2 said:
You mean can be ? if not :
I = Identity matrix
I' = Inverse
I'AI = A
which means A is diagonlaziable. We just found matrix I that Diagonalize A.

No, it was deliberately chosen to be NON-diagonalizable. Look again: the element ##a_{12} \neq 0##. The matrix ##A## is a 3x3 Jordan canonical form for eigenvalues -5,-5,0, with a non-diagonal Jordan block in the top left 2x2 location.

The eigenvalue -5 has algebraic multiplicity 2 but geometric multiplicity 1; that means that you cannot find three linearly-independent eigenvectors, only two. You need to include a so-called "generalized" eigenvector in order to have a full 3-dimensional basis. That is why the matrix cannot be diagonalized.
 
  • #20
Ray Vickson said:
No, it was deliberately chosen to be NON-diagonalizable. Look again: the element ##a_{12} \neq 0##. The matrix ##A## is a 3x3 Jordan canonical form for eigenvalues -5,-5,0, with a non-diagonal Jordan block in the top left 2x2 location.
Where did that a12 =1 came from ?
and how do you know that the Geometric multiplicity of -5 is 1 ?
 
  • #21
Dank2 said:
Where did that a12 =1 came from ?
and how do you know that the Geometric multiplicity of -5 is 1 ?
This ##a_{12}=1## has been willingly chosen, as I have chosen ##diag(-5,-5,0).##
However, as @Ray Vickson has said: it is the Jordan normal form for matrices (basically achieved by choosing a basis in which a matrix can be written as the sum of a diagonal and a nilpotent (strict upper triangular) matrix) which allow you to see the dimensions of eigenspaces. Diagonalizability depends on whether the geometric and algebraic multiplicities of eigenvalues coincide or not (see above).

These are two examples of possible matrices ##A##, one of which is diagonal and one is not diagonalizable.
(I haven't done it so far but it might be possible to make a finite conclusion if we take additionally the fact that ##p(A+5I)=1## into account?)
 
  • Like
Likes   Reactions: Dank2
  • #22
fresh_42 said:
This ##a_{12}=1## has been willingly chosen, as I have chosen ##diag(-5,-5,0).##
However, as @Ray Vickson has said: it is the Jordan normal form for matrices (basically achieved by choosing a basis in which a matrix can be written as the sum of a diagonal and a nilpotent (strict upper triangular) matrix) which allow you to see the dimensions of eigenspaces. Diagonalizability depends on whether the geometric and algebraic multiplicities of eigenvalues coincide or not (see above).

These are two examples of possible matrices ##A##, one of which is diagonal and one is not diagonalizable.
(I haven't done it so far but it might be possible to make a finite conclusion if we take additionally the fact that ##p(A+5I)=1## into account?)
We don't have nilpotent in our course, is possible to see that the geometric multiplicity of -5 is 2 or 1 ?
 
  • #23
Ray Vickson said:
If
A = \pmatrix{-5 &amp; 1 &amp; 0 \\ 0 &amp; -5 &amp; 0 \\ 0 &amp; 0 &amp; 0}
then all the conditions are met and ##A## cannot be diagonalized.
but Rank of A+5I is = Rank of A
 
  • #24
Dank2 said:
We don't have nilpotent in our course, is possible to see that the geometric multiplicity of -5 is 2 or 1 ?
Therefore I added "strict upper triangular".
Assume you have a matrix ##S## for which ##SAS^{-1} = B## where ##B## is either a diagonal matrix or of the form Ray suggested, i.e. has only two eigenvectors, one to the eigenvalue ##0## and one for ##-5##. Then examine ##A+5I## and remember it has to be of rank one.
 
  • #25
fresh_42 said:
Therefore I added "strict upper triangular".
Assume you have a matrix ##S## for which ##SAS^{-1} = B## where ##B## is either a diagonal matrix or of the form Ray suggested, i.e. has only two eigenvectors, one to the eigenvalue ##0## and one for ##-5##. Then examine ##A+5I## and remember it has to be of rank one.
see post #23,
 
Last edited:
  • #26
Dank2 said:
see post #23,
You already know the defect of ##A+5I## that is the dimension of its kernel. What happens to a basis of this kernel, if you apply ##A##?
 
  • #27
fresh_42 said:
You already know the defect of ##A+5I## that is the dimension of its kernel. What happens to a basis of this kernel, if you apply ##A##?
Sorry , dimension of it's kernel? you mean the dim of the nullspace of A+5I ?
 
  • #28
Dank2 said:
Sorry , dimension of it's kernel? you mean the dim of the nullspace of A+5I ?
Yes. Take basis vectors ##v_i## of it and calculate ##Av_i.##
 
  • #29
basis nullspace of A is v1 = (1,0,0),v2 = (0,1,0)
Av1 = -5*v1
Av2 = -5*v2
That is for A =
-5 0 0
0 -5 0
0 0 0
Because A as in post 23 doesn't satisfy p(A+5I) = 1
 
  • #30
Dank2 said:
basis nullspace of A is v1 = (1,0,0),v2 = (0,1,0)
Av1 = -5*v1
Av2 = -5*v2
That is for A =
-5 0 0
0 -5 0
0 0 0
Because A as in post 23 doesn't satisfy p(A+5I) = 1
Almost. It only guarantees you that ##SAS^{-1}## is of this form, i.e. ##A## is diagonalizable. And recalculate ##p(A+5I)## in this case. (It is also sufficient to state the linear independency of ##v_1## and ##v_2##. No need for coordinates.)
 

Similar threads

  • · Replies 32 ·
2
Replies
32
Views
3K
  • · Replies 14 ·
Replies
14
Views
4K
  • · Replies 7 ·
Replies
7
Views
3K
Replies
14
Views
2K
  • · Replies 69 ·
3
Replies
69
Views
10K
  • · Replies 11 ·
Replies
11
Views
5K
  • · Replies 18 ·
Replies
18
Views
4K
Replies
24
Views
4K
  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 2 ·
Replies
2
Views
5K