Prove A is Diagonalizable (Actual Question)

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In summary, diagonalizable matrices are square matrices that can be transformed into a diagonal matrix through a similarity transformation. To prove that a matrix is diagonalizable, we need to show that it can be represented as a diagonal matrix with the same eigenvalues as the original matrix. Diagonalizable matrices are significant in linear algebra because they simplify calculations and have useful geometric interpretations. However, not all matrices can be diagonalizable. Diagonalization has many real-world applications in fields such as physics, engineering, and economics.
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pezola
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[SOLVED] Prove A is Diagonalizable (Actual Question)

Homework Statement




Suppose that A [tex]\in[/tex] M[tex]^{nxn}[/tex](F) and has two distinct eigenvalues, [tex]\lambda[/tex][tex]_{1}[/tex] and [tex]\lambda[/tex][tex]_{2}[/tex], and that dim(E(subscript [tex]\lambda[/tex][tex]_{1}[/tex] ))= n-1. Prove that A is diagonalizable.


The Attempt at a Solution



So far, I know that dim(E subscript [tex]\lambda[/tex]2) [tex] \geq1[/tex]
and that
dim(E subscript [tex]\lambda[/tex]1) + dim(E subscript [tex]\lambda[/tex]2) [tex] \leq[/tex] n.
So dim(E subscript [tex]\lambda[/tex]2) = 1.

I am not exactly sure how this helps me to show A is digonalizable. Maybe I am thinking of something else and don't need this to prove A is diagonalizable. Please help.

(Also, sorry about my prevous blank post; I am new)
 
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  • #2
Welcome to PF!

pezola said:
So far, I know that dim(E subscript [tex]\lambda[/tex]2) [tex] \geq1[/tex]
and that
dim(E subscript [tex]\lambda[/tex]1) + dim(E subscript [tex]\lambda[/tex]2) [tex] \leq[/tex] n.
So dim(E subscript [tex]\lambda[/tex]2) = 1.

Hi pezola! Welcome to PF! :smile:

Your reasoning seems fine. :smile:

Can't you now use proof by induction - that is, assume the theorem is true for all numbers up to n - 1, and then prove it for n?
(Also, sorry about my prevous blank post; I am new)

:smile: … no problemo! … :smile:
 
  • #3
A matrix is diagonalizable iff its eigenvectors form a basis for the space (do you understand why?). You've pretty much shown this, maybe just another word or too on why the eigenvectors are independent.
 

1. What does it mean for a matrix to be diagonalizable?

Diagonalizable refers to a property of square matrices, where the matrix can be transformed into a diagonal matrix through a similarity transformation. This means that the matrix can be represented as a diagonal matrix with the same eigenvalues as the original matrix.

2. How can you prove that a matrix is diagonalizable?

To prove that a matrix A is diagonalizable, we need to show that there exists an invertible matrix P and a diagonal matrix D such that A = PDP^-1. This can be done by finding the eigenvalues and eigenvectors of A and using them to construct P and D.

3. What is the significance of diagonalizable matrices in linear algebra?

Diagonalizable matrices are important in linear algebra because they can simplify calculations and make it easier to solve linear systems of equations. They also have useful geometric interpretations and are closely related to the concept of eigenvalues and eigenvectors.

4. Can all matrices be diagonalizable?

No, not all matrices can be diagonalizable. A matrix is diagonalizable if and only if it has a complete set of linearly independent eigenvectors. If the matrix does not have enough linearly independent eigenvectors, then it cannot be diagonalizable.

5. How can diagonalization be used in real world applications?

Diagonalization has many practical applications, such as in physics, engineering, and economics. It can be used to simplify calculations and solve systems of differential equations, as well as to analyze and optimize systems with multiple variables. In economics, diagonalization can be used to simplify complex models and make predictions about future trends.

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