Linear Algebra / Geomtric Multiplicity

In summary, the conversation discusses the process of finding the eigenvalues, algebraic multiplicity, and geometric multiplicity of a 2x2 matrix A. The eigenvalue is found to be 1 with an algebraic multiplicity of 2, and the geometric multiplicity is determined to be 1. The conversation also briefly mentions finding P and J such that A=PJP^-1, but this process is not yet complete. The conversation ends with a question about choosing an eigenvector for the eigenvalue 1.
  • #1
Wildcat
116
0

Homework Statement


Let A = a 2x2 matrix row 1 [3/2 ½ ] row 2 [-½ ½]
a) Find the eigenvalue(s) of A
b) Find the algebraic multiplicity of each eigenvalue.
c)Find the geometric multiplicity of each eigenvalue.



Homework Equations





The Attempt at a Solution


I found the eigenvalue to be 1 with an algebraic multiplicity of 2.
I'm not sure about the geometric multiplicity. I think it is 1.
When I look at the dim(ker(A-1I)) I get ½x1 + ½x2=0 which I think means you have 1 degree of freedom therefore the geometric multiplicity is 1.
Can someone tell me if I'm doing this right?
 
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  • #2
That's right. 1 is a double eigenvalue but it only corresponds to a single eigenvector.
 
  • #3
Dick said:
That's right. 1 is a double eigenvalue but it only corresponds to a single eigenvector.

Thanks! I also have to find P and J such that A=PJP^-1 where J is the Jordan canonical form of A, but I'm not finished with that.
 
  • #4
Wildcat said:
Thanks! I also have to find P and J such that A=PJP^-1 where J is the Jordan canonical form of A, but I'm not finished with that.

I know I need to find the eigenvector corresponding to the eigenvalue 1, I found it to be
[1 -1] but I need another one. Do I choose one where (A-1I)v‡0, can I choose [1 0] for the other??
 
  • #5
Wildcat said:
I know I need to find the eigenvector corresponding to the eigenvalue 1, I found it to be
[1 -1] but I need another one. Do I choose one where (A-1I)v‡0, can I choose [1 0] for the other??

if I can do that, I get P=[1 1] [-1 0] J=[1 ½] [0 1] P^-1=[0 -1] [1 1] (these are 2x2 matrices)


which does = A [3/2 ½] [-½ ½] ?
 

1. What is the difference between algebraic multiplicity and geometric multiplicity?

The algebraic multiplicity of an eigenvalue is the number of times it appears as a root of the characteristic polynomial. The geometric multiplicity, on the other hand, is the dimension of the eigenspace associated with that eigenvalue. In other words, the geometric multiplicity is the number of linearly independent eigenvectors corresponding to that eigenvalue.

2. How do you calculate the geometric multiplicity of an eigenvalue?

To calculate the geometric multiplicity of an eigenvalue, you need to find the dimension of the corresponding eigenspace. This can be done by finding the nullity of the matrix (A - λI), where A is the original matrix and λ is the eigenvalue in question.

3. Can the geometric multiplicity of an eigenvalue be greater than its algebraic multiplicity?

Yes, it is possible for the geometric multiplicity to be greater than the algebraic multiplicity. This occurs when the eigenspace associated with that eigenvalue has a dimension that is greater than its multiplicity as a root of the characteristic polynomial.

4. How is the geometric multiplicity related to the diagonalizability of a matrix?

A matrix is diagonalizable if and only if the geometric multiplicity of each eigenvalue is equal to its algebraic multiplicity. In other words, a matrix is diagonalizable if it has a full set of linearly independent eigenvectors.

5. Why is the concept of geometric multiplicity important in linear algebra?

The geometric multiplicity provides important information about the structure and behavior of a matrix, particularly in relation to its eigenvectors and diagonalizability. It also plays a key role in the spectral theorem, which states that a symmetric matrix is diagonalizable and has real eigenvalues with corresponding orthogonal eigenvectors. Understanding the geometric multiplicity helps in solving systems of linear equations and in various applications of linear algebra in fields such as engineering, physics, and computer science.

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