Finding the directions of eigenvectors symmetric eigenvalue problem

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SUMMARY

The discussion centers on the symmetric eigenvalue problem represented by the equation Kv=w^2*v, where K is the stiffness matrix and M is the mass matrix. It emphasizes that the eigenvectors of the matrix K~ can be determined, with the understanding that the negatives of these eigenvectors are also valid eigenvectors. The concept of eigenspaces is introduced, highlighting that each eigenvalue corresponds to a one-dimensional eigenspace. Additionally, it is noted that in complex vector spaces, normalized eigenvectors can be expressed with a complex phase factor of unit modulus.

PREREQUISITES
  • Understanding of symmetric eigenvalue problems
  • Familiarity with stiffness and mass matrices in structural analysis
  • Knowledge of eigenspaces and eigenvectors
  • Basic concepts of complex numbers and their properties
NEXT STEPS
  • Study the computation of eigenvectors in symmetric matrices using MATLAB or Python's NumPy library
  • Explore the implications of eigenspaces in finite element analysis
  • Learn about the normalization of eigenvectors and its significance in quantum mechanics
  • Investigate the role of complex phase factors in eigenvector representation
USEFUL FOR

Mathematicians, engineers, and physicists involved in linear algebra, structural analysis, and quantum mechanics will benefit from this discussion.

Andrew1235
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Homework Statement
In the symmetric eigenvalue problem, K~v=w2v where K~=M−1/2KM−1/2, where K and M are the stiffness and mass matrices respectively.
Relevant Equations
K~v=w2v where K~=M−1/2KM−1/2
In the symmetric eigenvalue problem, Kv=w^2*v where K~=M−1/2KM−1/2, where K and M are the stiffness and mass matrices respectively. The vectors v are the eigenvectors of the matrix K~ which are calculated as in the example below. How do you find the directions of the eigenvectors? The negatives of the eigenvectors of a matrix are also eigenvectors of the matrix.
 

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Andrew1235 said:
How do you find the directions of the eigenvectors? The negatives of the eigenvectors of a matrix are also eigenvectors of the matrix.
When we talk about eigenvectors, we are really taking about eigenspaces. Each eigenvalue has an eigenspace of one or more dimensions associated with it. No single vector is the eigenvector. In this case, you have a 1D eigenspace associated with each eigenvalue.

The author has chosen normalised ##v_1, v_2##, which limits the choice to ##\pm v_1, \pm v_2##.

In complex vector spaces, a normalised eigenvector is determined only up to a complex "phase factor" of unit modulus. E.g. a normalised eigenvector can take the form ##\alpha v##, where ##v## is a normalised eigenvector and ##\alpha## is any complex number of unit modulus. And, of course, real numbers of unit modulus reduces to ##\pm 1##.
 

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