A question about finding vectors

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In summary, the conversation is discussing a problem about eigenvalues and eigenvectors, specifically how to create an orthogonal matrix using the given vectors. The original matrix is not clearly stated, but it is likely M=\left[\begin{array}{ccc}1 & 1 & 1 \\ 1 & 1 &1 \\ 1 & 1 & 1\end{array}\right]. The characteristic equation for this matrix is -\lambda^3+ 3\lambda^2= 0, with roots 0, 0, 3. To find eigenvectors corresponding to eigenvalue 0, the equation Mv=0 is used and two independent eigenvectors are found: [1
  • #1
transgalactic
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i added a link with the question and how i tried to solve it

i want to create a matrix using the vectors that i found

the problem is
that i got 4 vectors and two of them is (0,0,0)

http://img406.imageshack.us/my.php?image=img8228mh6.jpg
 
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  • #2
This is a problem about eigenvalues and eigenvectors, right?

Well, (0, 0, 0) is never an eigenvector since the whole point of an "eigenvalue", [itex]\lambda[/itex], of T is that there exist a non-zero vector v such that [itex]Tv= \lambda v[/itex].

You don't clearly state what the original matrix is but I think you mean
[tex]M=\left[\begin{array}{ccc}1 & 1 & 1 \\ 1 & 1 &1 \\ 1 & 1 & 1\end{array}\right][/tex]

The characteristic equation for that is not at all what you give, however. I get [itex]-\lambda^3+ 3\lambda^2= 0[/itex] which has roots 0, 0, 3. That makes sense to me since the matrix is badly "singular". It's no surprise that it has 0 as a double eigenvalue. To find eigenvectors corresponding to the eigenvalue 0, we look at
[tex]M=\left[\begin{array}{ccc}1 & 1 & 1 \\ 1 & 1 &1 \\ 1 & 1 & 1\end{array}\right]\left[\begin{array}{c} x \\ y \\ z \end{array}\right]= \left[\begin{array}{c} 0 \\ 0 \\ 0 \end{array}\right][/tex]

Obviously that gives us the single equation x+ y+ z= 0. The "solution space" is two dimensional- we can pick any values for x and y and solve for z. In particular if we take x= 1, y= 0, z= -1. An eigenvector corresponding to eigenvalue 0 is [1 0 -1]T. If we choose x= 0, y= 1, we get z= -1. An independent eigenvector corresponding to eigenvalue 0 is [0 1 -1]T.

To find an eigenvector corresponding to eigenvalue 3, we look at the equation
[tex]M=\left[\begin{array}{ccc}1 & 1 & 1 \\ 1 & 1 &1 \\ 1 & 1 & 1\end{array}\right]\left[\begin{array}{c} x \\ y \\ z \end{array}\right]= \left[\begin{array}{c} 3x \\ 3y \\ 3z \end{array}\right][/tex]
That gives three equations, x+ y+ z= 3x, x+ y+ z= 3y, and x+ y+ z= 3z which are, again, dependent equations. This time they reduce to x= y= z. An eigenvector corresponding to eigenvalue 3 is [1 1 1]T.

The matrix you are looking for, that diagonalizes M, is
[tex]M=\left[\begin{array}{ccc}1 & 0 & 1 \\ 0 & 1 &1 \\ -1 & -1 & 1\end{array}\right][/tex]
 
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  • #3
thanks

how to prove that this matrix is orthogonal
and if not
how to transform it to an orthogonal matrix

??
 
Last edited:
  • #4
See if it satisfies your definition of orthogonal, of course! There are several equivalent definitions (ATA= I is one) and I don't know which you were given. However, either as a definition, or as a theorem, it is true that the individual columns of an orthogonal matrix, as vectors, form an orthonormal basis for the vector space: the dot product of two different columns must be 0 and the dot product of a column with itself must be 1. Here, I made no attempt to reduce those vectors to an orthonormal basis:
[1 0 -1], [0 1 -1], and [1 1 1] happen to be "orthogonal" (their dot products are 0) but are not normalized. (One can show that eigenvectors corresponding to different eigenvalues must be orthogonal. The two eigenvectors corresponding to 0 were not necessarily orthogonal, but my choice of x=1 y=0 and x=0 y=1 forced that.) Find the length of each vector and divide each vector by its length. Using those "normalized" eigenvectors as columns will give you an orthogonal matrix.
 
  • #6
i have solved it again
but i don't get them to be orthogonal
what to do in that case??

i didnt get the dot product zero
(so its not orthogonal)

what to do??

and i have written bellow what what values i need to divide each vector in order to make
the matrix orthonormal
did i make it ok??

http://img267.imageshack.us/my.php?image=img8253ms5.jpg
 

1. What is a vector?

A vector is a mathematical concept that represents both magnitude (size) and direction. It is typically represented by an arrow pointing in the direction of the vector, with the length of the arrow representing the magnitude.

2. How do you find the magnitude of a vector?

The magnitude of a vector can be found using the Pythagorean theorem. This involves squaring the x-component of the vector, squaring the y-component, adding them together, and taking the square root of the result.

3. How do you find the direction of a vector?

The direction of a vector can be found using trigonometry. The tangent of the angle formed by the vector and the x-axis can be calculated by dividing the y-component by the x-component. The angle can then be found by taking the inverse tangent of this value.

4. What is the difference between a scalar and a vector?

A scalar is a quantity that only has magnitude, such as temperature or speed. A vector, on the other hand, has both magnitude and direction. In other words, a scalar is just a number while a vector has both a number and a direction associated with it.

5. How are vectors used in science?

Vectors are used in many different areas of science, including physics, engineering, and mathematics. They are particularly useful in describing the motion of objects, forces acting on objects, and the direction and magnitude of various physical quantities. Vectors are also used in fields such as computer graphics, where they are used to represent the movement of objects in a virtual space.

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