Find Corresponding Eigenvectors for Matrices A and B | Quick Help

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So, the eigenvectors are unchanged, only the eigenvalues are altered by the addition of 3. In summary, the matrix A has eigenvalues 1, 2, and 4 with corresponding eigenvectors [-1 0 1], [-4 1 4], and [2 3 1]. The matrix B, which is a transformation of A, has eigenvalues 4, 5, and 7 with the same eigenvectors. The eigenvectors remain unchanged, only the eigenvalues are altered by the addition of 3.
  • #1
rock.freak667
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Homework Statement



The matrix,A,given by
[tex]
A = \left(
\begin{array}{ccc}
7 & -4 & 6\\
2 & 2 & 2 \\
-3 & 4 & -2 \
\end{array}
\right)
[/tex]

has eigenvalues 1,2,4 . Find a set of corresponding eigenvectors.

Hence find the eigenvalues of B, where

[tex]
B = \left(
\begin{array}{ccc}
10 & -4 & 6\\
2 & 5 & 2 \\
-3 & 4 & 1 \
\end{array}
\right)
[/tex]

and state a corresponding set of eigenvectors.

Homework Equations





The Attempt at a Solution




Well I easily found the eigenvectors
[itex]
\lambda=1[/itex] corresponds to
[tex]
\left(
\begin{array}{c}
-1\\
0 \\
1\
\end{array}
\right)
[/tex]

[itex]
\lambda=2[/itex] corresponds to
[tex]
\left(
\begin{array}{c}
-4\\
1 \\
4\
\end{array}
\right)
[/tex]

[itex]
\lambda=4[/itex] corresponds to
[tex]
\left(
\begin{array}{c}
2\\
3 \\
1\
\end{array}
\right)
[/tex]


Well for the one with B, just solve det(b-[itex]\lambda[/itex]I)=0 to get the e.values... but it says to state a set of e.vectors meaning that I am not supposed to work them out.
The only thing I can really say about A and B is that in B all the elements in the main diagonal are the elements in the main diagonal of A with 3 added to them
 
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  • #2
In other words, B = A + 3I. Can you find a simple way to get B's eigenvalues and eigenvectors without explicitly working through B? Use the definitions of eigenvectors and eigenvalues.
 
  • #3
slider142 said:
In other words, B = A + 3I. Can you find a simple way to get B's eigenvalues and eigenvectors without explicitly working through B? Use the definitions of eigenvectors and eigenvalues.

Well I could say that

[itex]Det(B-\lamda I)=0[/itex] and put B=A+3I, then say that I can reduce A to a triangular matrix such that the diagonal elements(Eigenvalues of A)+(3-[itex]\lambda[/itex])=0 and solve from there. And get the e.values to be 7,4,5. But how would the e.vectors be altered?
 
Last edited:
  • #4
rock.freak667 said:
Well I could say that

[itex]Det(B-\lamda I)=0[/itex] and put B=A+3I, then say that I can reduce A to a triangular matrix such that the diagonal elements(Eigenvalues of A)+(3+[itex]\lambda[/itex])=0 and solve from there. And get the e.values to be 7,4,5. But how would the e.vectors be altered?

The determinant is just a way to extract the eigenvalues mechanically. The original definition should state that L is an eigenvalue of A iff Av = Lv for some vector v (an eigenvector of A), or equivalently, A - LI = 0. Thus, to find eigenvalues L, we are looking for solutions of the equation B - LI = 0, which is equivalent to A + (3 - L)I = 0. We already know the solutions of this equation, specifically we know that L - 3 = L' where L' is an eigenvalue of A. No messing around with matrix forms or determinants necessary. Similarly, you can use the original equation to see how the eigenvectors of A compare to the eigenvectors of B. Can you see the way from here? :)
 
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  • #5
I got the same answer but it all amounts to the same thing I guess.thanks

But then the e.vectors would be unchanged?
 
  • #6
rock.freak667 said:
I got the same answer but it all amounts to the same thing I guess.thanks

But then the e.vectors would be unchanged?

Indeed, you get the equation Av = (L - 3)v, where L is an eigenvalue of B and v is an eigenvector of B, implying that whenever v is an eigenvector for A, it is also an eigenvector for B, albeit with a different eigenvalue.
 
Last edited:

FAQ: Find Corresponding Eigenvectors for Matrices A and B | Quick Help

What are eigenvectors and why are they important?

Eigenvectors are special vectors that remain unchanged in direction when multiplied by a particular square matrix. They are important because they help us understand the behavior and transformations of a system. They are also used in a variety of applications such as image processing, data analysis, and quantum mechanics.

How do I find eigenvectors?

To find eigenvectors, you first need to find the eigenvalues of the matrix. This can be done by solving the characteristic equation of the matrix. Once you have the eigenvalues, you can plug them back into the original matrix and solve for the corresponding eigenvectors using Gaussian elimination or other methods.

What is the difference between an eigenvector and a normal vector?

An eigenvector is a special type of vector that does not change direction when multiplied by a matrix. A normal vector, on the other hand, is a vector that is perpendicular to a surface or plane. While all eigenvectors are normal vectors, not all normal vectors are eigenvectors.

Can I have more than one eigenvector for a single eigenvalue?

Yes, it is possible to have multiple eigenvectors for a single eigenvalue. This is because an eigenvalue represents a scalar factor that can be applied to any eigenvector to produce another eigenvector. In fact, an infinite number of eigenvectors can exist for a single eigenvalue.

How are eigenvectors used in data analysis?

Eigenvectors are commonly used in data analysis to reduce the dimensionality of a dataset. This is done by transforming the data into a new coordinate system defined by the eigenvectors of the covariance matrix. This allows for easier visualization and analysis of the data, as well as better understanding of the underlying relationships between variables.

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