If $a$ is a scalar and $M$ is a square matrix, it is very convenient to be able to write $a + M$. Usually people know immediately what this means, but are uneasy about “abusing” notation, so here’s the detailed justification for why this is perfectly legitimate:
Matrices should be considered first and foremost as linear transformations. You know what a matrix is if you know what it does to vectors.

A week ago someone asked whether the singular values of a general (real) matrix are the magnitudes of its eigenvalues. There are various ways to see that the answer is no, but here’s an amusingly nonconstructive proof:
Consider a random matrix $A$ taken from $GL(n)$ with some smooth distribution. With probability 1 all singular values of $A$ will be unique. However, with nonzero probability $A$ will be near a rotation matrix and will have a complex conjugate pair of eigenvalues with the same magnitude.

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