Reduction of quadratic form (principal axis)

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Homework Help Overview

The discussion revolves around the reduction of a quadratic form to a simple sum of squares, specifically focusing on the equation involving variables x1, x2, and x3. The original poster attempts to diagonalize a symmetric matrix derived from the quadratic form but encounters nonzero off-diagonal elements in their results.

Discussion Character

  • Exploratory, Assumption checking, Mathematical reasoning

Approaches and Questions Raised

  • The original poster describes their approach involving eigenvalues and eigenvectors, including the use of Gram-Schmidt orthogonalization. They express confusion over obtaining complex eigenvalues and not achieving a diagonalized matrix. Other participants question the assumptions made regarding the nature of the eigenvalues and the properties of symmetric matrices.

Discussion Status

Some participants provide insights regarding the properties of symmetric matrices, suggesting that the original poster may have made an error in their calculations or assumptions. There is acknowledgment of differing results from various computational tools, indicating a productive exploration of the problem.

Contextual Notes

Participants mention the use of different computational tools, such as Maxima and TI-89, and discuss the implications of obtaining complex versus real solutions in the context of symmetric matrices. There is an underlying concern about reliance on computational aids for algebraic components of the problem.

xman
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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)<br /> \left(\begin{array}{ccc}<br /> 2 & 1 & 0 \cr<br /> 1 & 2 & 1 \cr<br /> 0 & 1 & 1 <br /> \end{array} \right) <br /> \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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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.
 
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.
 
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.
 
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.
 

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