  The approximate joint diagonalization of a set of matrices consists in
finding a basis in which these matrices are as diagonal as possible. This
problem naturally appears in several statistical learning tasks such as blind
signal separation. We consider the diagonalization criterion studied in a
seminal paper by Pham (2001), and propose a new quasi-Newton method for its
optimization. Through numerical experiments on simulated and real datasets, we
show that the proposed method outper-forms Pham's algorithm. An open source
Python package is released.
