Solving 2D Linear Map with Eigenvectors & Values

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In summary, you are looking to find the eigenvectors and eigenvalues of a square matrix, and then solve for a solution using those eigenvectors.
  • #1
franky2727
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two part question the first part i understand the second part i dont

first part is find the eigenvectors and values of the matrix (4,-1 then below-4,4)

giving me lamda=2 (1,2)T and lamda=6 (1,-2)T

after this we have part 2 that i don't really understand how to do, I've done it before but have since forgot how to and can't find the corresponding notes

B Use this result to find the sollution to the two dimensional linear map

Xn+1= 4Xn-Yn

Yn+1=-4Xn+4Yn

with X0=1 and Y0=1

thanks in advance for help, i also have a similar question but without the limits which to be honest i have never fully understood so i'll try to wrap my head arround this one then post that one after
 
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  • #2
franky2727 said:
two part question the first part i understand the second part i dont

first part is find the eigenvectors and values of the matrix (4,-1 then below-4,4)

giving me lamda=2 (1,2)T and lamda=6 (1,-2)T

after this we have part 2 that i don't really understand how to do, I've done it before but have since forgot how to and can't find the corresponding notes

B Use this result to find the sollution to the two dimensional linear map

Xn+1= 4Xn-Yn

Yn+1=-4Xn+4Yn

with X0=1 and Y0=1

thanks in advance for help, i also have a similar question but without the limits which to be honest i have never fully understood so i'll try to wrap my head arround this one then post that one after

Since say, let A is a square matrix i.e. 2x2 matrix, then let

[tex]x_{n+1} = 4x_{n} - y_{n} [/tex] (1)
[tex]y_{n+1} = -4x_{n}+4y_{n} [/tex] (2)

then this is a recursive function, notice that the coefficients are equal to your original matrix.
================================================
Hint:
Let A be an nxn matrix. Let [tex]r_{k+1} = Ar_{k} , k=(0,1,2...) [/tex]

then to find a solution then take an eigenvector say [tex]r_{0}[/tex] and its eigenvalue [tex]\lambda [/tex] and let

[tex]r_{k}= \lambda ^{k}x_{0}[/tex]

Another hint: Let [tex]r_{0} = c_{1}v{1}+c_{2}v_{2}= [v_{1} v_{2}] \begin{bmatrix} c_{1} \\ c_{2} \end{bmatrix}[/tex]
What are the constants c? What are the vectors v1, v2?
 
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  • #3
sorry still totaly lost, could you go through it step by step so i can see what's going on?
 
  • #4
Couple of typos on my initial post. Reread it along with this post.

Notice that your matrix is

[tex]A= \begin{bmatrix} 4 & -1 \\4 & 4 \end{bmatrix}[/tex]

and that you are given [tex]r_{0} = \begin{bmatrix} 1 \\ 1 \end{bmatrix}[/tex]

let [tex]v_{1}[/tex] be your first eigenvector, and [tex]v_{2}[/tex] be your second eigenvector.

Notice that [tex]Av_{1}=\lambda_{1}v_{1}[/tex] and [tex]Av_{2} = \lambda_{2}v_{2}[/tex]

Then

[tex]\boxed{r_{k}=c_{1} \lambda_{1}^k v_{1} + c_{2} \lambda_{2}^k v_{2}}[/tex]

Remember, notice that [tex]Ar_{k} = A(\lambda)^kr_{0} = \lambda^k(Ar_{0}) =\lambda^k (\lambda r_{0}) = \lambda^{k+1}r_{0} = r_{k+1}[/tex]
Hope that helps
===============================
Your first job is to find the [tex]c_{1},c_{2}[/tex] since you've stated that you've already found the lambdas. The eigenvectors, v_1 v_2, you've already found.
 
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  • #5
Another way of looking at it is this: since your matrix, I'll call it A, has two independent eigenvectors, it can be "diagonalized". That is, there exist an invertible matrix U such that
[tex]UAU^{-1}]= U\left[\begin{array}{cc}4 & -1 \\ -4 & 4\end{array}\right]U^{-1}= \left[\begin{array}{cc}6 & 0 \\ 0 & 2\end{array}\right]= D[/tex]
and, of course, it follow from that that A= U-1DU. Now it should be easy to see that An= U-1DnU. For example, A2= (U-1DU)(U-1DU)= U-1D2U because the U-1 and U between the two "D"s cancel.

In fact, you can take U-1 to be the matrix having the eignvectors as columns:
[tex]\left[\begin{array}{cc}1 & 1 \\ -2 & 2\end{array}\right][/tex]
so that U is its inverse
[tex]\left[\begin{array}{cc}\frac{1}{2} & -\frac{1}{4} \\ \frac{1}{2} & \frac{1}{4}\end{array}\right][/tex]

You should be able to check that
[tex]\left[\begin{array}{cc}4 & -1 \\ -4 & 4\end{array}\right]=\left[\begin{array}{cc}1 & 1 \\ -2 & 2\end{array}\right]\left[\begin{array}{cc}6 & 0 \\ 0 & 2\end{array}\right]\left[\begin{array}{cc}\frac{1}{2} & -\frac{1}{4} \\ \frac{1}{2} & \frac{1}{4}\end{array}\right][/tex]
So that
[tex]\left[\begin{array}{cc}4 & -1 \\ -4 & 4\end{array}\right]^n=\left[\begin{array}{cc}1 & 1 \\ -2 & 2\end{array}\right]\left[\begin{array}{cc}6^n & 0 \\ 0 & 2^n\end{array}\right] \left[\begin{array}{cc}\frac{1}{2} & -\frac{1}{4} \\ \frac{1}{2} & \frac{1}{4}\end{array}\right][/tex]
Mulitplying out the right side should give you the solution.
 
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  • #6
so the information that i currently have is lamda1=2 lamda2=6 V1=(1,2)T and V2=(1,-2)T

What is r, K, and C and how do i go about finding them?
 
  • #7
so A^n=U^-1 . D^n . A . U

where do my X0 and Y0 values fall into this I'm still confused but this is definatly closer to the way i remember doing this
 
  • #8
im going to walk home now and get some food but ill check this again in about a hour thanks guys
 
  • #9
franky2727 said:
so the information that i currently have is lamda1=2 lamda2=6 V1=(1,2)T and V2=(1,-2)T

What is r, K, and C and how do i go about finding them?
?? No one has said anything about "r, K, and C". konthelion did say that the solution can be written in the form
[tex]{r_{k}=c_{1} \lambda_{1}^k v_{1} + c_{2} \lambda_{2}^k v_{2}}[/tex]
where c1 and c2 are constants to be determined by the "initial conditions (X0= Y0= 1), the two [itex]\lambda[/itex]s are your eigenvalues, 2 and 6, and v1 and v2 are the corresponding eigenvalues.

Since you got [itex]\lambda_1= 6[/itex] with [itex]v_1= \left[\begin{array}{c}1 \\ -2\end{array}\right][/itex] and [itex]\lambda_2= 2[/itex] with [itex]v_2= \left[\begin{array}{c} 1 \\ 2\end{array}\right][/itex], You have
[tex]r_n= c_1 6^n \left[\begin{array}1 \\ -2\end{array}\right]+ c_2 2^n \left[\begin{array}{c}1 \\ 2\end{array}\right][/tex]
Taking n= 0,
[tex]r_n= c_1 \left[\begin{array}1 \\ -2\end{array}\right]+ c_2 \left[\begin{array}{c}1 \\ 2\end{array}\right]=\left[\begin{array}{c}c_1+ c_2 \\-2c_1+ 2c_2\end{array}\right]= \left[\begin{array}{c}1 \\ 1\end{array}\right][/tex]
so we get the two equations c1+ c2= 1 and -2c1+ 2c2= 1. You can easily solve those to get c1= 1/4 and c2= 3/4. Then
[tex]r_n= \frac{1}{4} 6^n \left[\begin{array}1 \\ -2\end{array}\right]+ \frac{3}{4} 2^n \left[\begin{array}{c}1 \\ 2\end{array}\right][/tex]
[tex]= 6^n\left[\begin{array}{c}\frac{1}{4} \\-\frac{1}{2}\end{array}\right]+ 2^n\left[\begin{array}{c}\frac{3}{4} \\ \frac{3}{2}\end{array}\right][/tex]
so that [itex]x_n= (1/4)6^n+ (3/4)2^n[/itex] and [itex]y_n= (-1/2)6^n+ (1/2)2^n[/itex].

franky2727 said:
so A^n=U^-1 . D^n . A . U

where do my X0 and Y0 values fall into this I'm still confused but this is definatly closer to the way i remember doing this
Doing the calculation I suggested gives you a matrix. Now multiply that matrix by the vector (or column matrix)
[tex]\left[\begin{array}{c}x_0 \\ y_0 \end{array}\right]= \left[\begin{array}{c}1 \\ 1\end{array}\right][/tex]
That should give you the same solution as above.
 
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  • #10
nice one i get it now thanks, having my tea right now but after that i'll go through this question and then have a look at the question i have without limits to see if i can do that and if not i'll put that one up as well, thanks for the help
 
  • #11
i had a question yesterday entitled exam question help which 2 people were kind enough to work through with me to a point were i now understand how to do this question, as i stated on there yesterday i have another question that is similar but this time it is a bit more complex, could someone please take a look at this one and if possible try to present it in the same way that hallsofivey did yesterday as this was the way that i understood the question.

part (a) which i can do is find the solution to the two dimensional map

Xn+1=Xn -Yn
Yn+1=2Xn +4Yn

with X0=1 and Y0=1

giving me eivgenvalues of 2 and 3 with vectors of (1,-1)T and (1,-2)T respectivly

After which i do the question just the same as the one that was answered for me above and i end up with hopefully the correct answer of Xn=-2 . 3n+3 . 2n

and Yn=4.3n-3.2n

hopefully that bits right. then i have part b of the question which i don't know how to do which is

indicate how the second order map
Xn+1=4xn-3xn-1

can be expressed as a two dimensional map. by concidering the eigenvalues of the associated matrix show that Xn then there's a symbol that i have never seen before, it looks like a 8 on its side but part of the loop is missing on the right hand side then 3n for n large... thanks
 

FAQ: Solving 2D Linear Map with Eigenvectors & Values

What is a 2D linear map?

A 2D linear map is a mathematical function that transforms points in a two-dimensional space. It can be represented by a 2x2 matrix and is used to describe various physical phenomena, such as transformations in computer graphics or change in variables in physics.

What are eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are concepts used to analyze linear transformations. An eigenvector is a vector that does not change direction during the transformation. An eigenvalue is the scalar value that corresponds to the eigenvector and determines the amount of stretching or shrinking that occurs during the transformation.

How do eigenvectors and eigenvalues help in solving 2D linear maps?

Eigenvectors and eigenvalues help in solving 2D linear maps by providing a way to decompose the transformation into simpler components. They allow us to find the directions in which the transformation is most significant and the scale of the transformation in those directions. This information can be used to better understand and analyze the overall transformation.

What is the significance of the eigenvectors and eigenvalues in solving 2D linear maps?

The eigenvectors and eigenvalues give us important information about the behavior of a 2D linear map. They provide insight into the direction and magnitude of the transformation, and can be used to determine important properties such as fixed points and stability. They are also useful in applications such as image processing and data compression.

How do you find the eigenvectors and eigenvalues of a 2D linear map?

To find the eigenvectors and eigenvalues of a 2D linear map, we need to solve the characteristic equation of the transformation matrix. This involves finding the roots of the characteristic polynomial, which is a quadratic equation. The solutions of this equation will give us the eigenvalues, and the corresponding eigenvectors can be found by solving a system of linear equations using these eigenvalues.

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