# Singular Value Decomposition

Any matrix $M$ can be written as

$M = U \Sigma V^T,$

where $U$ and $V$ are orthogonal, and $\Sigma$ is diagonal with non-negative entries.

Because orthogonal and diagonal matrices have many convenient properties, it’s often simpler to replace a matrix with its SVD in order to analyze something.

## Problems

• In the problem of linear least squares, we are given a matrix $M$ (whose $n$ rows and $m$ columns encode $n$ data points each with $m$ features) and a vector $b$, and we seek a vector $x$ such that the length of $||Mx - b||$ is minimized.