Null and non null eigenvalues (Oinker's question at Yahoo Answers)

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In particular, as $\lambda\in K\setminus \{0\}$, $1$ is an eigenvalue of $B^{-1}$. In summary, The eigenvalues of the matrix D are 5 and -2, with corresponding eigenvectors (1,1)^T and (3,-4)^T respectively. If λ = 0 is an eigenvalue of B, then B is not invertible. And if B is not invertible, then λ = 0 is an eigenvalue of B. Additionally, if λ is an eigenvalue of B, then 1 is an eigenvalue of B^-1.
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Fernando Revilla
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Here is the question:

(a) Find the eigenvalues of the matrix D below. For each eigenvalue, find a corresponding eigenvector.

D =
2 3
4 1(b) Suppose B is an n×n matrix, and λ = 0 is an eigenvalue of B. Prove that B is not invertible.

(c) Suppose B is an n × n non-invertible matrix. Prove that λ = 0 is an eigenvalue of B.

(d) Suppose B is an invertible n × n matrix, and λ is an eigenvalue of B. Prove that 1 is
an eigenvalue of B−1.

Here is a link to the question:

Matrix Question?? a.b.c.d.? - Yahoo! Answers

I have posted a link there to this topic so the OP can find my response.
 
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  • #2
Hello Oinker,

$(a)\;$ Eigenvalues of $A=\begin{bmatrix}{2}&{3}\\{4}&{1}\end{bmatrix}$: $$\chi_A(\lambda)=\lambda^2-(\mbox{trace }A)\;\lambda+\det A=\lambda^2-3\lambda-10=0\\\Leftrightarrow \lambda=5\mbox{ (simple) }\vee\;\lambda=-2\mbox{ (simple) }$$ Eigenvectors and corresponding basis: $$\ker(A-5I)\equiv\left \{ \begin{matrix} -3x_1+3x_2=0\\4x_1-4x_2=0\end{matrix}\right.,\qquad B_5=\{(1,1)^T\}$$ $$\ker(A+2I)\equiv\left \{ \begin{matrix} 4x_1+3x_2=0\\4x_1+3x_2=0\end{matrix}\right.,\qquad B_{-2}=\{(3,-4)^T\}$$
$(b)\;$ If $\lambda=0$ is eigenvalue of $B$, then $\det (B-0I)=0$ so, $\det B=0$. As a consequence, $B$ is not invertible.

$(c)\;$ If $B$ is not invertible, $0=\det (B)=\det(B-0I)$ which implies that $\lambda=0$ is an eigenvalue of $B$.

$(d)\;$ According to $(b)$ and $(c)$, if $\lambda$ is an eigenvalue of $B$, necessarily $\lambda\neq 0$ and there exists $0\ne x\in K^n$ suach that $Bx=\lambda x$. Then, $$Bx=\lambda x\Rightarrow B^{-1}(Bx)=B^{-1}(\lambda x)\Rightarrow Ix=\lambda (B^{-1}x)\Rightarrow B^{-1}x=\frac{1}{\lambda}x$$ which implies that $1/\lambda$ is an eigenvalue of $B^{-1}$.
 

FAQ: Null and non null eigenvalues (Oinker's question at Yahoo Answers)

What is the difference between null and non-null eigenvalues?

Null eigenvalues are eigenvalues that have a value of zero, while non-null eigenvalues have a non-zero value. In other words, null eigenvalues represent vectors that do not change direction when multiplied by a matrix, while non-null eigenvalues represent vectors that do change direction.

What is the significance of null and non-null eigenvalues?

Null eigenvalues are significant because they represent vectors that lie in the null space of a matrix, which is important in understanding the linear dependence of a system. Non-null eigenvalues are significant because they represent eigenvectors that can be used to diagonalize a matrix, simplifying calculations and making it easier to understand the behavior of the system.

How do null and non-null eigenvalues relate to eigenvectors?

Null eigenvalues are associated with eigenvectors that lie in the null space of a matrix, while non-null eigenvalues are associated with eigenvectors that can be used to diagonalize a matrix. In both cases, the eigenvector is a vector that represents the direction of change for a particular system.

Can a matrix have both null and non-null eigenvalues?

Yes, a matrix can have both null and non-null eigenvalues. In fact, most matrices will have a combination of both types of eigenvalues. The number of null and non-null eigenvalues will depend on the size and properties of the matrix.

How are null and non-null eigenvalues used in practical applications?

Null and non-null eigenvalues are used in many areas of science and engineering, including physics, chemistry, and computer science. They are used to understand and analyze systems, such as electronic circuits, chemical reactions, and quantum systems. They are also used in machine learning and data analysis to identify patterns and relationships in data.

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