Lagrange to find eigen values and vectors?

In summary, the conversation was about finding eigenvalues and eigenvectors for a given matrix. The person mentioned that they had never used Lagrange multipliers for this problem and asked for hints. The expert summarized that the method for finding eigenvalues is to use the determinant equation and provided a resource for more information. The person then clarified that they wanted to use Lagrange multipliers and the expert explained how it could be used in this context.
  • #1
seto6
251
0

Homework Statement


im given a matrix A= 1 -2
///////////////////////
-2 4
im told to find the eigen values and the vectors... but the thing is i have never came across this, i learned lagrange multipliers but never used it to find eigen values and vector..

Homework Equations





The Attempt at a Solution



im really lost..

can someone hint me pls
thanks in advanve
 
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  • #2
To find the eigenvalues use:
[itex] det|A - I \lambda| = 0 [/itex]
Where I is the identity matrix.
Solve for lambda.
http://www.miislita.com/information-retrieval-tutorial/matrix-tutorial-3-eigenvalues-eigenvectors.html" is more information for finding Eigenvalues and their Eigenvectors:
 
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  • #3
Winzer said:
To find the eigenvalues use:
[itex] det|A - I \lambda| = 0 [/itex]
Where I is the identity matrix.
Solve for lambda.:

the mothod you states above, i know how to do it that way,but i need to do it using lagrange multiplier
 
  • #4
Please state the problem exactly as it is given. Normally, "Lagrange multipliers" are used to find maximum and minimum values, not for finding eigenvalues or eigenvectors.

However, you can think of a matrix as "stretching" or "compressing" directions in the xy-plane. Specifically, if [itex]\lambda[/itex] is the largest eigenvalue of matrix A, and v is a corresponding eigenvector, then [itex]Av= \lambda v[/itex] maximizes the length of Av. Given
[tex]v= \begin{bmatrix}x \\ y \end{bmatrix}[/tex],

[tex]\begin{bmatrix}1 & -2 \\ -2 & 4\end{bmatrix}\begin{bmatrix}x \\ y\end{bmatrix}= \begin{bmatrix}x- 2y \\ -2x+ 4y\end{bmatrix}[/tex]
will have maximum length (among all vectors with the same length) if and only if v is an eigenvector corresponding to the largest eigenvector.

That is, the problem of finding the larger and smaller eigenvalues can be construed as "Maximize (minimize) [itex](x- 2y)^2+ (-2x+ 4y)^2[/itex] subject to the constraint [itex]x^2+ y^2= 1[/itex]". That is a problem that can be handled with Lagrange multipliers.
 
  • #5
i thought i stated...but i see now that i stated in the title only sorry,
thanks for your help!
 

Related to Lagrange to find eigen values and vectors?

1. What is Lagrange to find eigen values and vectors?

Lagrange is a mathematical approach used to find the eigenvalues and eigenvectors of a square matrix. It involves solving a system of equations to determine the values that satisfy a particular characteristic polynomial.

2. Why is it important to find eigenvalues and eigenvectors using Lagrange?

Eigenvalues and eigenvectors are important in many areas of mathematics and science, including linear algebra, differential equations, and quantum mechanics. They provide valuable information about the behavior of a system and can be used to simplify complex calculations.

3. How does Lagrange to find eigen values and vectors work?

Lagrange involves setting up and solving a system of equations using the characteristic polynomial of a square matrix. The eigenvalues are the solutions to this polynomial, and the corresponding eigenvectors can be found by plugging the eigenvalues back into the original system of equations.

4. Can Lagrange be used for any type of matrix?

Yes, Lagrange can be used for any square matrix since it is based on the characteristic polynomial, which can be calculated for any square matrix.

5. Are there any limitations to using Lagrange to find eigen values and vectors?

One limitation of Lagrange is that it can only be used for square matrices. Additionally, finding the eigenvalues and eigenvectors can be computationally intensive for larger matrices, making it impractical for some applications.

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