An example of linear regression could look like:
$min \sum_{i=0}^{m}||x_i A - y_i||_2^{2}$, where ${x_i, y_i} \in \mathbb{R}^n$ and $A \in \mathbb{R}^{n\times n}$.
I am interested in knowing how do I solve such problem with an of the following extra constraints, $A$ is an orthogonal matrix or $A$ is a rotation matrix.
Notice that a rotation matrix is orthogonal.