Eigenvalues + Algebraic/Geometric Multiplicity

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SUMMARY

The discussion focuses on the concepts of algebraic and geometric multiplicity in the context of eigenvalues, specifically for a 3x3 matrix A with an eigenvalue λ=4. Algebraic multiplicity refers to the number of times the eigenvalue appears as a root in the characteristic polynomial, which in this case is 3. Geometric multiplicity is defined as the dimension of the eigenspace associated with the eigenvalue, indicating that there is only one linearly independent eigenvector corresponding to λ=4.

PREREQUISITES
  • Understanding of eigenvalues and eigenvectors
  • Familiarity with characteristic polynomials
  • Knowledge of linear algebra concepts
  • Basic matrix operations
NEXT STEPS
  • Study the characteristic polynomial of matrices
  • Learn about eigenspaces and their dimensions
  • Explore the relationship between algebraic and geometric multiplicity
  • Practice problems involving eigenvalues and eigenvectors
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Students studying linear algebra, educators teaching eigenvalue concepts, and anyone seeking to deepen their understanding of matrix theory and its applications.

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I'm studying for a linear algebra final, and I'm looking over an old final our prof gave us and I've come across something I don't remember ever hearing anything about... Here's the problem:

Write down a matrix A for the following condition:
A is a 3x3 matrix with lambda=4 with algebraic multiplicity 3 and with geometric multiplicity 1.

...I don't have a problem with eigenvalues or anything, but I don't believe he ever mentioned algebraic multiplicity or geometric multiplicity. Is this another concept in linear algebra?? Or is this something way simple that I'm looking way too far into.

...What does he mean by algebraic multiplicity and geometric multiplicity??


Thanks!
 
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Algebraic multiplicity is easy. It's the multiplicity of the root in the characteristic polynomial. I checked wikipedia (always a good first stab) for geometric multiplicity and it says that it is the dimension of the eigenspace. In other words, there is only one linearly independent eigenvector with value 4.
 
 
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