Eigenvalue for Orthogonal Matrix

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SUMMARY

The discussion centers on the properties of orthogonal matrices, specifically regarding eigenvalues and eigenvectors. It establishes that if Q is an orthogonal matrix with an eigenvalue λ₁ = 1, then the corresponding eigenvector x₁ is also an eigenvector of the transpose of Q, denoted as Qᵀ. The relationship Qx₁ = x₁ leads to the conclusion that (Qx₁)ᵀ = x₁ᵀQᵀ, confirming that x₁ remains an eigenvector under the transformation of Qᵀ.

PREREQUISITES
  • Understanding of orthogonal matrices and their properties
  • Familiarity with eigenvalues and eigenvectors
  • Knowledge of matrix transposition and its implications
  • Basic linear algebra concepts
NEXT STEPS
  • Study the properties of orthogonal matrices in detail
  • Learn about the spectral theorem for symmetric matrices
  • Explore the implications of eigenvalues in transformations
  • Investigate applications of orthogonal matrices in computer graphics
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Students and professionals in mathematics, particularly those studying linear algebra, as well as anyone interested in the theoretical foundations of matrix operations and their applications in various fields.

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Homework Statement



Let Q be an orthogonal matrix with an eigenvalue λ_{1} = 1 and let x be an eigenvector belonging to λ_{1}. Show that x is also an eigenvector of Q^{T}.

Homework Equations



Qx = λx where x \neq 0

The Attempt at a Solution



Qx_{1} = x_{1} for some vector x_{1}

(Qx_{1})^{T} = x_{1}^{T}Q^{T}


I'm kind of stuck with how to start this problem, as I'm not sure what I've done is even starting down the right path. Can anyone give me a nudge in the right direction?
 
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What's (Q^T)(Q) if Q is real orthogonal?
 
Thank you. I didn't realize what an orthogonal matrix was (yikes!). Once I did the proof fell right out of the definition.
 

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