Reduction of quadratic form (principal axis)

In summary, the conversation discusses the issue of getting nonzero off-diagonal elements when trying to reduce an equation to a simple sum of squares. The speaker suggests calculating the eigenvalues of the coefficient matrix, which leads to complex solutions. They then use Gram-Schmidt orthogonalization to form an orthonormal basis set and construct a matrix to diagonalize the system, but are not getting a diagonalized matrix. Another participant points out that the original equation has all real solutions and suggests using a TI-89 calculator. The conversation ends with the participants thanking each other for the tips and discussing the accuracy of different calculators in solving equations.
  • #1
xman
93
0
i keep getting nonzero off diagonal elements when i try to reduce to simple sum of squares, of the equation
[tex]2 x_{1}^{2}+2x_{2}^{2}+x_{3}^{2}+2x_{1}x_{3}+2x_{2}x_{3} [/tex]
what i have is
[tex] \left(\begin{array}{ccc} x_{1} & x_{2} & x_{3} \end{array}\right)
\left(\begin{array}{ccc}
2 & 1 & 0 \cr
1 & 2 & 1 \cr
0 & 1 & 1
\end{array} \right)
\left(\begin{array}{c} x_{1} \cr x_{2} \cr x_{3} \end{array} \right)
[/tex]
so my thought was to calculate the eigenvalues of the coefficient matrix above, which yield complex solutions from the characteristic equation
[tex] 1-6 \lambda+5 \lambda^{2}-\lambda^{3}=0 [/tex]
From the complex eigenvalues I obtain complex eigenvectors, which i'll post if necessary, but are rather lengthy. From the eigenvectors I choose to use Gram-Schmidt orthogonalization to form an orthonormal basis set. From which I construct a matrix with the corresponding basis set, and use diagonalize the system I have the diagonalization matrix
[tex] D = \left(\mid n \rangle \langle m \mid \right)^{T} A \left( \mid n \rangle \langle m \mid \right) [/tex]
where the matrix
[tex] \left(\mid n \rangle \langle m \mid \right) [/tex]
is the orthonormal eigenvector matrix. When I'm done with all of this I'm not getting a diagonalized matrix. I was wondering if I am making a mistake in my approach, or if anyone else does get a diagonalized matrix equation.
 
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  • #2
The equation 1-6x+5x^2-x^3=0 has all real solutions. You could have known this because your matrix is symmetric and symmetric matrices in the reals are orthogonally diagonalizable in the reals.
 
  • #3
0rthodontist said:
The equation 1-6x+5x^2-x^3=0 has all real solutions. You could have known this because your matrix is symmetric and symmetric matrices in the reals are orthogonally diagonalizable in the reals.
That's interesting, I was bad and usinig Maxima to calculate the roots of the equation and getting imaginary components, interesting, when I plot and find the roots, you're correct the roots are all real. Thanks, for the tip, I didn't know that about symmetric matrices, either. Thanks again.
 
  • #4
xman said:
That's interesting, I was bad and usinig Maxima to calculate the roots of the equation
:biggrin: I was using my TI-89.
 
  • #5
Perhaps the -89 is superior to my cas, even mathematica, maple given imaginary components, though on order of [tex]10^{-16}[/tex] or so. I wonder why that is. Not to mention I'm glad I'm not the only one whose cheats on algebra parts of problems...well that may get you into trouble as I found out today. Hey thanks again.
 

1. What is a quadratic form?

A quadratic form is a mathematical expression that contains variables squared and possibly other terms, but no higher powers. It can be written in the form of a polynomial equation, such as ax^2 + bxy + cy^2.

2. What is the principal axis of a quadratic form?

The principal axis of a quadratic form is the set of axes along which the quadratic form takes its maximum and minimum values. It is also known as the eigenvector axis, as it is determined by the eigenvectors of the associated matrix.

3. Why is it important to reduce a quadratic form to its principal axis?

Reducing a quadratic form to its principal axis allows for easier analysis and interpretation of the form. It simplifies the form by eliminating cross-product terms and allows for a better understanding of the behavior of the form along its maximum and minimum values.

4. How is a quadratic form reduced to its principal axis?

To reduce a quadratic form to its principal axis, the form is first expressed in matrix form. The matrix is then diagonalized using eigenvalue and eigenvector analysis. The eigenvectors become the principal axis, and the eigenvalues determine the maximum and minimum values of the form along those axes.

5. What are some applications of reducing quadratic forms to their principal axis?

Reducing quadratic forms to their principal axis has applications in various fields, including statistics, economics, and physics. It is used in multivariate analysis, optimization problems, and in determining the principal axes of objects in physics. It also has applications in computer graphics and machine learning.

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