What Does the Book Say About the Eigenvalues of 3x3 Matrices?

In summary, the conversation discusses the existence of eigenvalues in a quadratic matrix 3x3. The book mentioned states that every quadratic matrix 3x3 has at least one eigenvalue, but the conversation questions if it should be specified as at least one real eigenvalue. The conversation also brings up the possibility of a complex 3x3 matrix having only one eigenvalue, citing the example of a diagonal matrix with all entries equal to i. The conversation suggests that the book may be discussing real matrices, but without further information it is unclear what the book is specifically referring to.
  • #1
LagrangeEuler
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I found in one book that every quadratic matrix 3x3 has at least one eigenvalue. I do not understand. Shouldn't be stated at least one real eigenvalue? Thanks for the answer.
 
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  • #2
LagrangeEuler said:
I found in one book that every quadratic matrix 3x3 has at least one eigenvalue. I do not understand. Shouldn't be stated at least one real eigenvalue? Thanks for the answer.
Yes. I assume that the book is primarily assuming real matrices.

We get a characteristic polynomial which decomposes into linear factors in case of an algebraic closed field. So we have ##\chi(t)=-(t-\lambda_1)(t-\lambda_2)(t-\lambda_3)##. But we do not have any knowledge whether the algebraic multiplicities are all one. E.g. ##\lambda_1=\lambda_2=\lambda_3## cannot be ruled out, what commonly is called one eigenvalue, not three.
 
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  • #3
By definition eigenvalues have to belong to the base field. A qubic polynomial with real quoeficients always has a real root.
 
  • #4
Yes. But if we have complex 3x3 matrix is it possible to have only one eigenvalue?
 
  • #5
LagrangeEuler said:
Yes. But if we have complex 3x3 matrix is it possible to have only one eigenvalue?
Like the diagonal matrix ##diag(i, i, i)##?
 
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  • #6
LagrangeEuler said:
Yes. But if we have complex 3x3 matrix is it possible to have only one eigenvalue?
And what is the book talking about? May be if you revieled the title and the page or quoted the book, we wouldn't have to guess.
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts used in linear algebra to describe the behavior of a linear transformation. Eigenvalues represent the scaling factor of the eigenvector when it is transformed by the linear transformation.

2. How do you calculate eigenvalues of a 3x3 matrix?

To calculate the eigenvalues of a 3x3 matrix, you first need to find the determinant of the matrix. Then, you need to solve the characteristic equation using the values of the determinant. The solutions to the characteristic equation are the eigenvalues of the matrix.

3. Why are eigenvalues important?

Eigenvalues are important because they provide information about the behavior of a linear transformation. They can tell us about the scaling factor and direction of the transformation, and can also help us understand the stability and dynamics of systems in physics, engineering, and other fields.

4. Can a matrix have complex eigenvalues?

Yes, a matrix can have complex eigenvalues. In fact, if a matrix has complex coefficients, it is likely to have complex eigenvalues. This is because the characteristic equation may have complex solutions, leading to complex eigenvalues.

5. How many eigenvalues can a 3x3 matrix have?

A 3x3 matrix can have up to 3 eigenvalues. This is because the characteristic equation of a 3x3 matrix is a cubic equation, which can have up to 3 distinct solutions. However, a 3x3 matrix can also have repeated eigenvalues, in which case it would have fewer than 3 distinct eigenvalues.

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