Find 2 eigenvectors given an eigenvalue, and find remaining eigenvalue

In summary, the conversation discussed solving a system of equations for eigenvectors, using the basic definition of "eigenvalue" and a useful trick for splitting vectors into linear combinations.
  • #1
imsleepy
49
0
Could someone please walk me through answering this question:

PfNOw.png
it looks easy but i forgot how to do it -__-

i did A - lamda I = 0, substituted lamda = 5, then my matrix became

0 4 4
0 -2 -2
0 -2 -2

so i put in the form of A|b where b was a zero vector and i row reduced, getting
0 1 1 | 0
0 0 0 | 0
0 0 0 | 0

and now i don't know what to do.

apparently the answer is {(1,0,0),(0,-1,1)}

any help please?

edit: there have been some silly errors to other solutions for this particular past paper, like a missing - sign. just a heads up in case the answer is a tad wrong.
 
Last edited:
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  • #2
imsleepy said:
Could someone please walk me through answering this question:

PfNOw.png



it looks easy but i forgot how to do it -__-

i did A - lamda I = 0, substituted lamda = 5, then my matrix became

0 4 4
0 -2 -2
0 -2 -2

so i put in the form of A|b where b was a zero vector and i row reduced, getting
0 1 1 | 0
0 0 0 | 0
0 0 0 | 0

and now i don't know what to do.
This is a system of one equation in three variables, so there are two variables that are free.

The nonzero row represents the equation x2 + x3 = 0.
To get all three variables involved, we can write
x1 = x1
x2 = -x3
x3 = x3

So any vector x that is an eigenvector for the eigenvalue 5 is a linear combination of two vectors. Can you see them here?
imsleepy said:
apparently the answer is {(1,0,0),(0,-1,1)}

any help please?

edit: there have been some silly errors to other solutions for this particular past paper, like a missing - sign. just a heads up in case the answer is a tad wrong.
 
Last edited:
  • #3
i sort of get it...

what do you mean when you say x3 = x ?

the worked solutions that i have say eigenvectors are:

(x1,x2,x3) = (x1,-x3,x3)
which makes sense to me

but then they split it up:
(x1,x2,x3) = (x1,-x3,x3) = (x1,0,0) + (0,-x3,x3)

then take x1 and x3 out the front as arbitrary non-zero constants (which i get).

Why do they split up (x1,-x3,x3), is it only because the question is asking for 2 eigenvectors?
and in that case can you just split it up any way so that when you add them they still equal to (x1,-x3,x3)?
 
  • #4
imsleepy said:
i sort of get it...

what do you mean when you say x3 = x ?
That should have said x3 = x3. I neglected to get the subscript in between my [ sub] tags. I fixed it in my earlier post.
imsleepy said:
the worked solutions that i have say eigenvectors are:

(x1,x2,x3) = (x1,-x3,x3)
which makes sense to me

but then they split it up:
(x1,x2,x3) = (x1,-x3,x3) = (x1,0,0) + (0,-x3,x3)

then take x1 and x3 out the front as arbitrary non-zero constants (which i get).

Why do they split up (x1,-x3,x3), is it only because the question is asking for 2 eigenvectors?
and in that case can you just split it up any way so that when you add them they still equal to (x1,-x3,x3)?

They split <x1, -x3, x3> to get two vectors.
<x1, -x3, x3> = x1<1, 0, 0> + x3<0, -1, 1>. Since x1 and x3 are arbitrary, this represents all linear combinations of <1, 0, 0> and <0, -1, 1>.

(All vectors are column vectors here.>
 
  • #5
ohhhhhhhhh i get it now, they split it up into the 2 vectors, one vector includes only the x1 component and the other one only includes the x3 components.

thanks a lot :)
 
  • #6
Keep that trick in mind! It comes in handy in a lot of problems...
 
  • #7
Personally, I prefer to use the basic definition of "eigenvalue" to find the eigenvectors.
"[itex]\lambda[/itex] is an eigenvalue of A if and only if there exist a non-zero vector v such that [itex]Av= \lambda v[/itex]".

If 5 is an eigenvalue the there exist a vector <x, y, z> such that
[tex]\begin{bmatrix}5 & 4 & 4 \\ 0 & 3 & -2 \\ 0 & -2 & 3\end{bmatrix}\begin{bmatrix}x \\ y \\ z\end{bmatrix}= \begin{bmatrix}5x \\ 5y \\ 5z\enmd{bmatrix}

Which is the same as the three equations 5x+ 4y+ 4z= 5x, 3y- 2z= 5y, and -2y+ 3z= 5z or 4y+ 4z= 0, -2z= 2y, and -2y= 2z, all of which reduce to y= -z. Since there is no "x" in those equations, x can be anything and so an eigenvector can be written as <x, y, -y>= <x, 0, 0>+ <0, y, -y>= x<1, 0, 0>+ y<0, 1, -1>
 

1. What is an eigenvalue and eigenvector?

An eigenvalue is a special number associated with a square matrix that represents a scalar factor by which an eigenvector is scaled. An eigenvector is a non-zero vector that, when multiplied by a square matrix, gives a scalar multiple of itself as a result.

2. How do you find eigenvalues and eigenvectors?

To find eigenvalues, you need to solve the characteristic equation of the matrix, which is det(A-λI)=0, where A is the matrix, λ is the eigenvalue, and I is the identity matrix. To find eigenvectors, you need to substitute the eigenvalues into the equation A-λI x v=0 and solve for v, where v is the eigenvector.

3. Why is finding eigenvalues and eigenvectors important?

Eigenvalues and eigenvectors are important in many areas of mathematics and science, particularly in linear algebra and differential equations. They can help in understanding the behavior of systems, such as in physics and engineering, and in data analysis and machine learning.

4. Can a matrix have more than two eigenvalues and eigenvectors?

Yes, a matrix can have multiple eigenvalues and corresponding eigenvectors. The number of eigenvalues and eigenvectors is equal to the number of rows or columns in the matrix. A matrix can also have repeated eigenvalues and eigenvectors, which can affect the behavior of the system.

5. How is the remaining eigenvalue found if one eigenvalue is already known?

If one eigenvalue is known, the remaining eigenvalue can be found by using the fact that the sum of all eigenvalues is equal to the trace of the matrix. Therefore, the remaining eigenvalue can be calculated by subtracting the known eigenvalue from the trace of the matrix.

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