Eigenvectors of a 3x3 Matrix A: Calculation and Verification

In summary, Fred is discussing the eigenvalues and eigenvectors of a matrix and his calculations for the third eigenvector are incorrect. He is advised to check his eigenvalues again. In a separate conversation, he asks about finding a matrix P that is invertible and satisfies P^-1 AP. It is noted that the zero vector is not an eigenvector and Fred realizes he had typed his matrix incorrectly.
  • #1
Mathman23
254
0
Hi

I have this here matrix

[tex]A = \left[ \begin{array}{ccc} 2 & 1 & 0 \\ 0 & 1 & 0 \\ 3 & 3 & 0 \end{array} \right][/tex]

I calculate the eigenvalues and get (2,1,-1)

Next I calculate the eigenvectors and get (1,0,1) and (-1,1,0) and (0,0,0)

My professor says my third eigenvector is wrong and it should (0,0,1)

My calculation:


[tex]A = \left[ \begin{array}{ccc} (2-(-1) & 1 & 0 \\ 0 & (1-(-1) & 0 \\ 3 & 3 & 1-(-1) \end{array} \right]
= \left[ \begin{array}{ccc} 3 & 1 & 0 \\ 0 & 2 & 0 \\ 3 & 3 & 0 \end{array} \right][/tex]

Then according to the theorem regarding eigenvectors:

[tex]\left[ \begin{array}{ccc} 3 & 1 & 0 \\ 0 & 2 & 0 \\ 3 & 3 & 0 \end{array} \right] \left[ \begin{array}{c} v_1 \\ v_2 \\ v_3 \end{array} \right] = \left[ \begin{array}{c} 0 \\ 0 \\ 0 \end{array} \right] [/tex]

then

[tex]3v_1 + v_2 = 0[/tex]

[tex] 2v_2 = 0[/tex]

[tex]3v_1 + 3 v_2 = 0[/tex]

Is my calculations correct ??

sincerley and best regards,

Fred
 
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  • #2
First of all, the zero vector is never an eigenvector (even though it might behave like one). If x is an eigenvector "belonging" to the eigenvalue -1, then Ax = -x, or equivalently (A + I)x = 0. The right-most entry in the bottom row of this equation:

[tex]\left[ \begin{array}{ccc} 3 & 1 & 0 \\ 0 & 2 & 0 \\ 3 & 3 & 0 \end{array} \right] \left[ \begin{array}{c} v_1 \\ v_2 \\ v_3 \end{array} \right] = \left[ \begin{array}{c} 0 \\ 0 \\ 0 \end{array} \right] [/tex]

is wrong, there should be a 1 there instead of a 0.
 
Last edited:
  • #3
Check your eigenvalues again. I get [tex]\lambda = 0,1,2[/tex]
 
  • #4
Hi and many thanks for Your answer.

I have a second question I hope You can answer for me.

Finding a matrix P which is invertible and which complies with P^-1 AP ? ?

Isn't P then span of the three eigenvector ?

Sincerley and Best Regards,

Fred

Muzza said:
First of all, the zero vector is never an eigenvector (even though it might behave like one). If x is an eigenvector "belonging" to the eigenvalue -1, then Ax = -x, or equivalently (A + I)x = 0. The right-most entry in the bottom row of this equation:

[tex]\left[ \begin{array}{ccc} 3 & 1 & 0 \\ 0 & 2 & 0 \\ 3 & 3 & 0 \end{array} \right] \left[ \begin{array}{c} v_1 \\ v_2 \\ v_3 \end{array} \right] = \left[ \begin{array}{c} 0 \\ 0 \\ 0 \end{array} \right] [/tex]

is wrong, there should be a 1 there instead of a 0.
 
  • #5
Corneo said:
Check your eigenvalues again. I get [tex]\lambda = 0,1,2[/tex]


Hi again

thats because I typed my matrix wrong

[tex]A = \left[ \begin{array}{ccc} 2 & 1 & 0 \\ 0 & 1 & 0 \\ 3 & 3 & -1 \end{array} \right][/tex]

Sincerley

Fred
 

What is an eigenvector?

An eigenvector is a vector that, when multiplied by a given square matrix, results in a scalar multiple of itself. In other words, the direction of the eigenvector is unchanged by the matrix transformation, but its magnitude is scaled.

What is the importance of eigenvectors?

Eigenvectors are important in many fields of science and engineering, including physics, chemistry, economics, and computer science. They are used to model and understand complex systems, such as quantum mechanics, fluid dynamics, and network analysis.

What is the eigenvector problem?

The eigenvector problem is a mathematical problem that involves finding the eigenvectors of a given matrix. It is often used in linear algebra and other fields of mathematics to solve various problems, such as finding the stability of a system or determining the dominant factors in a data set.

How is the eigenvector problem solved?

The eigenvector problem is typically solved by finding the eigenvalues of the matrix first, and then using those values to find the corresponding eigenvectors. This can be done through various methods, such as using the characteristic polynomial or using iterative algorithms like the power method or the QR algorithm.

What are some real-world applications of the eigenvector problem?

The eigenvector problem has many real-world applications, including image and signal processing, data compression, pattern recognition, and machine learning. It is also used in finance for portfolio optimization and risk management. In addition, the eigenvector problem has practical applications in engineering, such as designing efficient structures and analyzing electrical circuits.

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