Spectral decomposition

Symmetric matrices are always (orthogonally) diagonalizable.

That is, for any symmetric matrix $\mathbf{A} \in \mathbb{R}^{n \times n}$, there exists an orthogonal matrix $\mathbf{Q} = [\mathbf{q}_1...\mathbf{q}_n]$ and a diagonal matrix $\mathbf{\Lambda} = \mathrm{diag} (\lambda_1,…,\lambda_n)$, both real and square, such that $$\mathbf{A} = \mathbf{Q\Lambda Q}^{\sf T}$$ where $\lambda_i$’s are the eigenvalues of $\mathbf{A}$ and $\mathbf{q}_i$’s the corresponding eigenvectors (orthogonal to each other and with unit norm).

Such a factorization is called the eigendecomposition of $\mathbf{A}$, also called the spectral decomposition of $\mathbf{A}$.

For general rectangular matrices, there is Singular value decomposition.

References:

1. https://www.sjsu.edu/faculty/guangliang.chen/Math253S20/lec5svd.pdf
2. https://people.math.carleton.ca/~kcheung/math/notes/MATH1107/wk10/10_symmetric_matrices.html
3. https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix