Newbetuts

Given a $4\times 4$ symmetric matrix, is there an efficient way to find its eigenvalues and diagonalize it?

Does non-symmetric positive definite matrix have positive eigenvalues?

A simple explanation of eigenvectors and eigenvalues with 'big picture' ideas of why on earth they matter

What exactly are eigen-things?

Simultaneous diagonalization of commuting linear transformations

Matrices commute if and only if they share a common basis of eigenvectors?

Eigenvalues of the rank one matrix $uv^T$

How to prove that eigenvectors from different eigenvalues are linearly independent

Diagonalizable transformation restricted to an invariant subspace is diagonalizable

Show that the determinant of $A$ is equal to the product of its eigenvalues

Are the eigenvalues of $AB$ equal to the eigenvalues of $BA$? (Citation needed!)

Why is the eigenvector of a covariance matrix equal to a principal component?

Intuitively, what is the difference between Eigendecomposition and Singular Value Decomposition?

What is the difference between "singular value" and "eigenvalue"?

Proof that the trace of a matrix is the sum of its eigenvalues

New posts in eigenvalues-eigenvectors

Riemann hypothesis: is Bender-Brody-Müller Hamiltonian a new line of attack?

What is the relation between rank of a matrix, its eigenvalues and eigenvectors

What is the largest eigenvalue of the following matrix?

Similar matrices have the same eigenvalues with the same geometric multiplicity

Geometric interpretation for complex eigenvectors of a 2×2 rotation matrix

Given a $4\times 4$ symmetric matrix, is there an efficient way to find its eigenvalues and diagonalize it?

Does non-symmetric positive definite matrix have positive eigenvalues?

A simple explanation of eigenvectors and eigenvalues with 'big picture' ideas of why on earth they matter

What exactly are eigen-things?

Simultaneous diagonalization of commuting linear transformations

Matrices commute if and only if they share a common basis of eigenvectors?

Eigenvalues of the rank one matrix $uv^T$

How to prove that eigenvectors from different eigenvalues are linearly independent

Diagonalizable transformation restricted to an invariant subspace is diagonalizable

Show that the determinant of $A$ is equal to the product of its eigenvalues

Are the eigenvalues of $AB$ equal to the eigenvalues of $BA$? (Citation needed!)

Why is the eigenvector of a covariance matrix equal to a principal component?

Intuitively, what is the difference between Eigendecomposition and Singular Value Decomposition?

What is the difference between "singular value" and "eigenvalue"?

Proof that the trace of a matrix is the sum of its eigenvalues