Eigenvalues and diagonalizability

In summary, for an n x n matrix A to have eigenvalues, its columns must be linearly dependent in order for a nontrivial solution to exist. However, for A to be diagonalizable, its columns must be linearly independent. This may seem contradictory as neither of these statements is always true. A matrix can have eigenvalues even if its columns are independent, and it can be diagonalizable even if its columns are not independent. The basis for the eigenspace of the former would be the null space, while the basis for the latter would be the column space since no free variables exist.
  • #1
fk378
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This is a concept question..
I'm having trouble understanding why for an n x n matrix A, in order to have eigenvalues, it must have linearly dependent columns (so that a nontrivial solution exists), but for the same A, in order to be diagonalizable, the columns must be linearly INdependent.

The basis for the eigenspace of the former would be the null space, but for the latter, the basis would be the column space since no free variables exist.
 
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  • #2
It may be difficult to understand because neither of those statements is true. If the columns (or rows) are linearly independent, then it has an INVERSE. A noninvertible matrix can have plenty of eigenvalues. Nor do the columns have to be independent for it to be diagonalizable. The zero matrix is perfectly diagonalizable.
 

What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts that are used to analyze linear transformations. Eigenvalues represent the scaling factor of the eigenvectors, which are the vectors that remain in the same direction after undergoing a transformation.

What is diagonalizability?

Diagonalizability is a property of a square matrix where it can be transformed into a diagonal matrix through a change of basis. This means that the matrix has a set of linearly independent eigenvectors that can be used to represent the matrix in a simpler form.

How do you calculate eigenvalues and eigenvectors?

The process for calculating eigenvalues and eigenvectors involves finding the eigenvalues first, which can be done by solving the characteristic equation det(A - λI) = 0. Then, the eigenvectors can be found by solving the system of equations (A - λI)x = 0 for each eigenvalue λ.

Why are eigenvalues and eigenvectors important?

Eigenvalues and eigenvectors are important in many areas of mathematics and science, including linear algebra, differential equations, and quantum mechanics. They provide a way to simplify complex matrices and understand the behavior of linear transformations.

What is the significance of a matrix being diagonalizable?

A matrix being diagonalizable has several important implications. It means that the matrix has a complete set of linearly independent eigenvectors, which can be used to simplify the matrix and make calculations easier. It also allows for easier analysis of the behavior of the matrix and its transformation properties.

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