Proof of one of the properties of a matrix

In summary, the determinant of a matrix is equal to the product of all its eigenvalues, which can be shown by factoring the characteristic polynomial into linear factors and plugging in \lambda = 0. The notation for the eigenvalues may be confusing, but it is important to remember that they are constants in the characteristic equation.
  • #1
mess1n
24
0
Hey, I've come across a part of my notes which states:

Statement:The determinant of a matrix is equal to the product of all its eigenvalues:

Proof: We know that, after opening up the determinant of A - [tex]\ell[/tex]E, we get a polynomial algebraic equation in [tex]\ell[/tex] which has solutions [tex]\ell[/tex][tex]^{1}[/tex],...,[tex]\ell[/tex][tex]^{p}[/tex], i.e. one can write:

det(A-[tex]\ell[/tex]E) = ([tex]\ell[/tex][tex]^{1}[/tex] - [tex]\ell[/tex])...([tex]\ell[/tex][tex]^{p}[/tex] - [tex]\ell[/tex])

By putting [tex]\ell[/tex] = 0 in the above equation, we can obtain the desired result.

I understand that if [tex]\ell[/tex] = 0, then what's left is detA = [tex]\ell[/tex][tex]^{1}[/tex][tex]\ell[/tex][tex]^{2}[/tex]...[tex]\ell[/tex][tex]^{p}[/tex]. Or at least, I assume that's what my lecturer is getting at.

What I don't understand is why the [tex]\ell[/tex] in the characteristic equation (A-[tex]\ell[/tex]E) is the same as the [tex]\ell[/tex]'s in the ([tex]\ell[/tex][tex]^{p}[/tex] - [tex]\ell[/tex]), but not the same as the [tex]\ell[/tex][tex]^{p}[/tex]'s in ([tex]\ell[/tex][tex]^{p}[/tex] - [tex]\ell[/tex]).

Why doesn't detA = 0 ?

I'd really appreciate any help.

[note: I couldn't find a lambda symbol so I had to use [tex]\ell[/tex] instead]

Cheers,
Andrew
 
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  • #2
I think the notation is poor. What you have written as

[tex]\lambda^k[/tex]

is meant to be the [itex]k'th[/itex] eigenvalue, NOT [itex]\lambda[/itex] to the k'th power. It would be more customary to use subscripts instead of superscripts to avoid this ambiguity.

The characteristic equation is by definition

[tex]\mathrm{det}(A - \lambda I) = 0[/tex]

(I'm using the more standard [itex]I[/itex] for the identity matrix, instead of [itex]E[/itex].)

The left-hand side of this equation is a polynomial in [itex]\lambda[/itex] of degree exactly [itex]n[/itex] (the characteristic polynomial). Since its degree is [itex]n[/itex], it can be factored into [itex]n[/itex] linear factors:

[tex]\mathrm{det}(A - \lambda I) = (\lambda - \lambda_1)(\lambda - \lambda_2)\cdots(\lambda - \lambda_n)[/tex]

where the [itex]\lambda_k[/itex]'s are the [itex]n[/itex] roots of the characteristic polynomial, which are by definition the eigenvalues of [itex]A[/itex]. In particular, with respect to the VARIABLE [itex]\lambda[/itex], the eigenvalues [itex]\lambda_1,\ldots,\lambda_n[/itex] are constants, so plugging in [itex]\lambda = 0[/itex] does not affect them.
 
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Related to Proof of one of the properties of a matrix

1. What is a matrix and what are its properties?

A matrix is a rectangular array of numbers or symbols arranged in rows and columns. Its properties include dimensions (number of rows and columns), elements (individual numbers or symbols), and operations (addition, subtraction, multiplication).

2. How do you prove that a matrix has a specific property?

To prove a matrix has a specific property, you can use algebraic manipulations and mathematical operations to show that the property holds true for all elements in the matrix. This can be done by using specific examples or general proofs.

3. What is an example of a property that a matrix can have?

An example of a property of a matrix is being invertible, meaning that it has an inverse matrix that when multiplied together results in the identity matrix.

4. Can a matrix have more than one property?

Yes, a matrix can have multiple properties. For example, a matrix can be both symmetric (where the elements above the main diagonal are equal to the elements below) and diagonal (where all non-diagonal elements are zero).

5. How are properties of a matrix useful in real-world applications?

The properties of a matrix are essential in various fields of science, such as engineering, physics, and computer science. They are used in solving systems of equations, representing transformations, and analyzing data. Understanding the properties of a matrix allows scientists to manipulate and use them effectively in real-world applications.

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