There has been extensive study of diagonalization of matrices. Diagonalization can be viewed as using a similarity transform to concentrate the magnitude of all entries within as small a subset of entries as possible. We motivate and present results in our talk on what can be viewed as reversing this process, namely spreading out the magnitudes of entries as uniformly as possible.

The talk is based on joint work with Antwan Clark, Bryan A. Curtis, and Leslie Hogben