H Math Blog: Understanding Eigenvalues and Characteristic Polynomials

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In summary, the eigenvalues of a matrix are the solutions to the characteristic polynomial, which is found by taking the determinant of the matrix with the variable x substituted for the eigenvalue. If the determinant is not 0, then the matrix has an inverse and the equation (A-xI)v=0 has a unique solution. However, this contradicts the definition of an eigenvalue, which requires non-trivial solutions.
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jeff1evesque
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Hello,

I was reading something in my text/wikipedia, and they both said that "...the eigenvalues of a matrix are the zeros of its characteristic polynomial." Do they mean that λ in the characteristic polynomial causes det (A - λI) = 0 (in particular A = λI)?

JL
 
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The characteristic polynomial is p(x) = det(A - xI). The zeroes of the characteristic polynomial are the x values that satisfy det(A-xI) = 0.
 
  • #3
Yes, that's exactly what they mean.

If the determinant of [itex]A- \lambda I[/itex] is not 0, then [itex]A- \lambda I[/itex] has an inverse and so the equation [itex](A- \lambda I)v= 0[/itex] has a unique solution [itex](A- \lambda I)^{-1}(A- \lambda I)v= v= (A- \lambda I)^{-1}0= 0[/itex] which contradicts the definition of "eigenvalue" which is that the equation [itex]Av= \lambda v[/itex], equivalent to [itex](A- \lambda I)v= 0[/itex], has "non-trivial" (non-zero) solutions.
 

FAQ: H Math Blog: Understanding Eigenvalues and Characteristic Polynomials

What is an eigenvalue?

An eigenvalue is a number that represents the scaling factor of a vector when it is multiplied by a linear transformation. In simpler terms, it is a value that helps us understand how a transformation affects the direction of a vector.

How is an eigenvalue calculated?

An eigenvalue is calculated by solving the characteristic equation det(A - λI) = 0, where A is a square matrix and λ is the eigenvalue. This equation is used to find the values of λ that satisfy the equation, which are then considered the eigenvalues of the matrix A.

What is the significance of eigenvalues?

Eigenvalues are significant because they provide important information about the properties of a matrix. They can tell us about the stability of a system, the direction and magnitude of a transformation, and the behavior of a matrix under certain operations.

How are eigenvalues used in real-world applications?

Eigenvalues have many applications in fields such as physics, engineering, and computer science. They are used in image and signal processing, data analysis, and in solving differential equations. They are also important in understanding the behavior of complex systems.

Can a matrix have complex eigenvalues?

Yes, a matrix can have complex eigenvalues. In fact, if a matrix has real coefficients, it must have a complex eigenvalue if it is not symmetric. Complex eigenvalues are important in understanding the behavior of systems with oscillating or rotating components.

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