A matrix is diagonalizable when algebraic and geometric multiplicities are equal

In summary, a matrix is considered diagonalizable when the algebraic multiplicity of its eigenvalues is equal to its geometric multiplicity. This was proven in class, but the explanation may not have been fully understood. The algebraic multiplicity refers to the number of eigenvalues, counting for multiplicity, and for an n by n matrix, this value will always be n. The geometric multiplicity is the number of independent eigenvalues, and when it is equal to the algebraic multiplicity, there will be n independent eigenvectors. This results in a diagonal matrix when the linear transformation represented by the matrix is written in terms of these eigenvectors.
  • #1
jack_bauer
10
0
A matrix is diagonalizable when algebraic and geometric multiplicities are equal.
My professor proved this in class today, but I did not fully understand his explanation and proof. Can someone please help?
 
Physics news on Phys.org
  • #2
I thought I had answered this before. The "algebraic multiplicity" is the number of eigenvalues, counting "multiplicity". If follows that, including complex eigenvalues, since every polynomial can be factored into linear factors over the complex numbers, that the "algebraic multiplicity" of an n by n matrix is n. The "geometric multiplicity" is the number of independent eigenvalues. Thus, if the algebraic multiplicity is the same as the geometric multiplicity, the geometric multiplicity is also n and there exist n independent eigenvectors. But the "underlying" vector space of an n by n matrix has dimension n so those eigenvectors form a basis for that vector space. Writing the linear transformation this matrix represents in that vector space gives a diagonal matrix.
 
  • #3
Jack, find the eigenvalues and eigenvectors of A by hand(!)

[tex]A = \begin{pmatrix}2 &1 &0\\0 &2 &0\\0 &0 &2\end{pmatrix}[/tex]
 

What does it mean for a matrix to be diagonalizable?

When a matrix is diagonalizable, it means that it can be transformed into a diagonal matrix through a similarity transformation, where the new matrix has the same eigenvalues as the original matrix.

What are the algebraic and geometric multiplicities of a matrix?

The algebraic multiplicity of an eigenvalue of a matrix is the number of times the eigenvalue appears as a root of the characteristic polynomial. The geometric multiplicity is the number of linearly independent eigenvectors corresponding to that eigenvalue.

Why do the algebraic and geometric multiplicities need to be equal for a matrix to be diagonalizable?

If the algebraic and geometric multiplicities are not equal, it means that there are not enough linearly independent eigenvectors to fully diagonalize the matrix. In other words, there are not enough eigenvectors to span the entire matrix, which is necessary for a similarity transformation to occur.

What happens if a matrix is not diagonalizable?

If a matrix is not diagonalizable, it means that it cannot be transformed into a diagonal matrix through a similarity transformation. This can occur if the algebraic and geometric multiplicities are not equal or if there are not enough eigenvectors to span the entire matrix. In this case, the matrix may have a Jordan canonical form instead.

How can I determine if a matrix is diagonalizable?

To determine if a matrix is diagonalizable, you can calculate its eigenvalues and corresponding eigenvectors. If the number of linearly independent eigenvectors equals the number of distinct eigenvalues (i.e. the algebraic and geometric multiplicities are equal), then the matrix is diagonalizable. Otherwise, it is not diagonalizable.

Similar threads

Replies
4
Views
2K
  • Linear and Abstract Algebra
Replies
10
Views
2K
  • Linear and Abstract Algebra
Replies
5
Views
1K
  • Math Proof Training and Practice
Replies
2
Views
2K
  • Linear and Abstract Algebra
Replies
10
Views
981
Replies
2
Views
3K
  • Linear and Abstract Algebra
Replies
9
Views
1K
  • Linear and Abstract Algebra
Replies
2
Views
5K
  • Linear and Abstract Algebra
Replies
4
Views
2K
  • Linear and Abstract Algebra
Replies
6
Views
1K
Back
Top