A seemingly simple linear algebra equation that eludes me

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Discussion Overview

The discussion revolves around a linear algebra equation related to eigenvalues and eigenvectors of a symmetric matrix. Participants explore the relationships between various matrix elements and their implications, particularly focusing on the expressions for the cofactor matrix and the normalization of eigenvectors.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant presents an equation involving a symmetric matrix and its eigenvector, questioning the derivation of a specific relationship between the eigenvector components and the cofactors.
  • Another participant suggests a potential connection between the terms in the equations and the inverse of a matrix, referencing a previous discussion.
  • A different participant claims to have derived a relationship involving the ratios of the eigenvector components and the cofactors, leading to a conclusion about the sum of the cofactors.
  • One participant expresses confidence in the method of calculating the inverse of a matrix, while also questioning the dimensional consistency of the equation presented.
  • Another participant clarifies that the eigenvector is normalized, implying it is dimensionless.
  • A participant asks for definitions of the terms E and K, seeking clarification on their roles in the equations.
  • One participant expresses confusion regarding the relevance of the trace of the inverse matrix in the context of the discussion.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the relationships and definitions discussed. Multiple competing views and interpretations of the equations remain, with some participants questioning the validity of others' claims.

Contextual Notes

There are unresolved questions regarding the definitions of certain terms (E and K) and the dimensional analysis of the equations presented. The discussion includes various assumptions and interpretations that have not been fully clarified.

ytht100
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It is from some famous publications. But I seem can get it from rigorous proof after many hours and different methods of trying and Googling.

If we have g as the eigenvector of a symmetric matrix and G is the eigenvalue of the symmetric matrix.
[tex]\left[ {\begin{array}{*{20}{c}} {X11 - G}&{X12}&{X13}\\ {X12}&{X22 - G}&{X23}\\ {X13}&{X23}&{X33 - G} \end{array}} \right]\left[ {\begin{array}{*{20}{c}} {g1}\\ {g2}\\ {g3} \end{array}} \right] = 0[/tex]

and:
[tex]\begin{array}{l}<br /> E11 = (X22 - G)(X33 - G) - X_{23}^2\\<br /> E22 = (X11 - G)(X33 - G) - X_{13}^2\\<br /> E33 = (X11 - G)(X22 - G) - X_{12}^2<br /> \end{array}[/tex]

Then:
[tex]g1g1 = E11/(E11 + E22 + E33)?[/tex]

Why?

Thanks a lot!
 
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Your answer may not be correct. I have figured out the answer:
g1/g3 = E13/E33 =(g1g3K)(g3g3K)
g2/g3 = E23/E33 =(g2g3K)(g3g3K)

Manipulations of similar equations we get: E11=g1g1K, E22=g2g2K, E33=g3g3K ... (1)

So E11+E22+E33 =(g1g1+g2g2+g3g3) K = K ...(2)

From (1) and (2), we obtain E11=g1g1 (E11+E22+E33)
 
I'm almost 100% certain the inverse of a matrix can be calculated this way although it's been a long time I did it. Have you recognized that the ##A_{ij}## which are obtained by calculating the determinant of ##A## with it's ##i-th## row and ##j-th## column stripped have a) to be transposed and b) have the sign ##(-1)^{i+j}##?

So your ##E_{ii}## are really the diagonal elements of ##(X - diag(-G,-G,-G))^{-1}##. But I admit I don't know how this might help. Perhaps you can use the facts that invertible symmetric matrices are symmetric again and that they can be diagonalized. Their trace is the sum of the eigenvalues, the determinate the product.

How exactly is your matrix ##E## defined and are you sure you want to calculate the squared first coordinate of g? If you - just to have a control - think of g as a 3-dimensional vector measured in meter, your equation then has m^2 on its left and no unit on its right side. Looks a little odd.
 
Last edited:
g is dimensionless because it is normalized.
 
How are ##E## and K defined?
 
I have listed E11, E22, and E33 already. E is defined as the cofactor matrix of the left side of the matrix at the first equation. K is an assumed number from g1/g3 = E13/E33 =(g1g3)(g3g3)= (g1g3K)(g3g3K).
 
Sorry I have absolute no idea where the trace of ##(X - G)^{-1}## comes into play.
 

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