## What is the eigendecomposition of a real symmetric matrix?

In linear algebra, eigendecomposition is the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors. Only diagonalizable matrices can be factorized in this way.

## What are the eigenvalues of a symmetric matrix?

crucial properties: ▶ All eigenvalues of a real symmetric matrix are real. orthogonal. complex matrices of type A ∈ Cn×n, where C is the set of complex numbers z = x + iy where x and y are the real and imaginary part of z and i = √ −1.

**How do you decompose a symmetric matrix?**

Prove that, without using induction, A real symmetric matrix A can be decomposed as A=QTΛQ, where Q is an orthogonal matrix and Λ is a diagonal matrix with eigenvalues of A as its diagonal elements.

**Is every real symmetric matrix diagonalizable?**

Real symmetric matrices not only have real eigenvalues, they are always diagonalizable. In fact, more can be said about the diagonalization.

### What is eigendecomposition of covariance matrix?

Just like Cholesky decomposition, eigendecomposition is a more intuitive way of matrix factorization by representing the matrix using its eigenvectors and eigenvalues. In the case of covariance matrix, all the eigenvectors are orthogonal to each other, which are the principal components for the new feature space.

### What is a real symmetric matrix?

In linear algebra, a real symmetric matrix represents a self-adjoint operator over a real inner product space. The corresponding object for a complex inner product space is a Hermitian matrix with complex-valued entries, which is equal to its conjugate transpose.

**Why eigenvalues of symmetric matrix are real?**

If x is an eigenvalue of A with eigenvalue λ, we have x∗Ax=x∗(λx)=λx∗x. Since x∗Ax and x∗x are always real (and x∗x is not zero for an eigenvector x), this means λ must be real too. Since A is symmetric, ˉxTAx=(Ax)Tˉx=xTATˉx=xTAˉx.

**Can any symmetric matrix be decomposed?**

For the second question, the answer is “No”. The product of a matrix and its transpose is symmetric. So a non-symmetric positive definite matrix cannot be decomposed into such a product. For the first question, we can do better.

## Why real symmetric matrix is diagonalizable?

Symmetric matrices are diagonalizable because there is an explicit algorithm for finding a basis of eigenvectors for them. The key fact is that the unit ball is compact.

## When can a matrix be diagonalized?

A square matrix is said to be diagonalizable if it is similar to a diagonal matrix. That is, A is diagonalizable if there is an invertible matrix P and a diagonal matrix D such that. A=PDP^{-1}.

**What is the use of eigendecomposition?**

Eigendecomposition is used to decompose a matrix into eigenvectors and eigenvalues which are eventually applied in methods used in machine learning, such as in the Principal Component Analysis method or PCA.

**What is eigen decomposition?**

Eigen Decomposition. The matrix decomposition of a square matrix into so-called eigenvalues and eigenvectors is an extremely important one. This decomposition generally goes under the name “matrix diagonalization.”.

### What is eigenvalue decomposition?

In linear algebra, eigendecomposition or sometimes spectral decomposition is the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors. Only diagonalizable matrices can be factorized in this way.

### Are the eigenvectors of similar matrices the same?

Two similar matrices have the same eigenvalues, however, their eigenvectors are normally different. See: eigenvalues and eigenvectors of a matrix. The characteristic polynomial and the minimum polynomial of two similar matrices are the same. A matrix and its transpose are similar.

**Can real symmetric matrix have complex eigenvectors?**

In general, it is normal to expect that a square matrix with real entries may still have complex eigenvalues. One may wonder if there exists a class of matrices with only real eigenvalues. This is the case for symmetric matrices. The proof is very technical and will be discussed in another page.