Proving det(A) = lambda_1 * lambda_2 * ... * lambda_n for Eigenvalue A

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In summary, the conversation discusses how to prove the equation det(A) = \lambda_1*\lambda_2*...*\lambda_n, where \lambda_i represents the eigenvalues of A. It is mentioned that this can be shown for symmetrical square matrices using certain assumptions. However, it is also noted that this may not hold true for other cases. The conversation ends with an explanation of why det(A) is the product of the n eigenvalues of A.
  • #1
Niels
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How do you prove that [itex]det(A) = \lambda_1*\lambda_2*...*\lambda_n[/itex], where [itex]\lambda_i[/itex] is the eigenvalues of A? I'm stuck :cry:
 
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  • #2
It isn't true, so you can't prove it. You should examine the question carefully.
 
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  • #3
Niels said:
How do you prove that [itex]det(A) = \lambda_1*\lambda_2*...*\lambda_n[/itex], where [itex]\lambda_i[/itex] is the eigenvalues of A? I'm stuck :cry:

For that to happen,u must make certain assumptions on the matrix 'A'.
The most important is that the matrix 'A' is of square form.If it is symmetrical,then:
a)if A has real elements,then exists a nonsingular orthogonal matrix M which can bring A to diagonal form:
[tex] \exists M\in O_{n}(R) [/tex],so that [tex]MAM^{T}=A_{diag} [/tex]
Then it's easy to show that det A=det A_{diag}=product of eigenvalues.
b)if A has complex elements,then exists a unitary matrix Z which can bring A to diagonal form
[tex] \exists Z\in U_{n}(C) [/tex],so that [tex]ZAZ^{\dagger}=A_{diag} [/tex]
Again,it's easy to show that the eigenvalues are on the diagonal and hence the det.is the product of eigenvalues.

Daniel.
 
  • #4
Well,Matt,if u're right and I'm wrong,then I'm going to kill my QFT teacher since he graduated both physics and maths. :mad: For a square,symmetrical matrix it has to be true.For other cases (meaning square form and nonsymetry),probably not.

Daniel.
 
  • #5
No, we're both correct, I said you should be careful, and you showed something inthe special case the matrix is diagonalizable, which is in some sense the notion I meant when I said that you should be careful. This depends upon how we dsitinguish between algebraic and geometric multiplicity.
 
  • #6
Ok,sorry here's the whole text:
Let A be an nxn matrix, and suppose A har n real eigenvalues [itex]\lambda_1 ... \lambda_n[/itex] repeated accordingly to multiplicities, so that
[tex] det(A - \lambda I) = (\lambda_1 - \lambda)*(\lambda_2 - \lambda)*...*(\lambda_n - \lambda)[/tex]
Explain why det(A) is the product of the n eigenvalues of A.
(Hint: the equation holds for all [itex]\lambda[/itex])
 
  • #7
let lambda = 0
 
  • #8
Thanks! I know now that I'm stupied :)
 

Related to Proving det(A) = lambda_1 * lambda_2 * ... * lambda_n for Eigenvalue A

1. What is an Eigenvalue and why is it important?

An Eigenvalue is a scalar value that represents how a linear transformation changes a vector. It is important because it helps us understand the behavior of a system or matrix and make predictions about its future behavior.

2. How do I calculate Eigenvalues?

To calculate Eigenvalues, you need to find the roots of a characteristic polynomial of the matrix or system. This can be done by solving the characteristic equation or using a numerical method such as the QR algorithm.

3. What is the significance of Eigenvalues in data analysis?

In data analysis, Eigenvalues are used to reduce the dimensionality of a dataset by identifying the most important features or variables. They are also used in principal component analysis to transform data into a lower-dimensional space.

4. Can you provide an example of Eigenvalues in real life?

One example of Eigenvalues in real life is in population dynamics. The Eigenvalues of a population matrix can tell us whether a population will grow or decline over time, and the corresponding Eigenvectors can show us which age groups are contributing the most to the population change.

5. How can I use Eigenvalues to solve differential equations?

Eigenvalues can be used to solve systems of linear differential equations. By finding the Eigenvalues and Eigenvectors of the coefficient matrix, we can reduce the system to a diagonal form and then solve for the individual equations. This approach is known as diagonalization.

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