Multiplication law and S transform for non-Hermitian random matrices.

We derive a multiplication law for free non-Hermitian random matrices allowing for an easy reconstruction of the two-dimensional eigenvalue distribution of the product ensemble from the characteristics of the individual ensembles. We define the corresponding non-Hermitian S transform being a natural generalization of the Voiculescu S transform. In addition, we extend the classical Hermitian S transform approach to deal with the situation when the random matrix ensemble factors have vanishing mean including the case when both of them are centered. We use planar diagrammatic techniques to derive these results.

Spectrum of the product of independent random Gaussian matrices.

We show that the eigenvalue density of a product X=X1X2...XM of M independent NxN Gaussian random matrices in the limit N-->infinity is rotationally symmetric in the complex plane and is given by a simple expression rho(z,z)=1/Mpisigma(-2/M)|z|(-2+(2/M)) for |z|sigma. The parameter sigma corresponds to the radius of the circular support and is related to the amplitude of the Gaussian fluctuations. This form of the eigenvalue density is highly universal. It is identical for products of Gaussian Hermitian, non-Hermitian, and real or complex random matrices. It does not change even if the matrices in the product are taken from different Gaussian ensembles. We present a self-contained derivation of this result using a planar diagrammatic technique. Additionally, we conjecture that this distribution also holds for any matrices whose elements are independent centered random variables with a finite variance or even more generally for matrices which fulfill Pastur-Lindeberg's condition. We provide a numerical evidence supporting this conjecture.

Correlations of eigenvectors for non-Hermitian random-matrix models.

We establish a general relation between the diagonal correlator of eigenvectors and the spectral Green's function for non-Hermitian random-matrix models in the large-N limit. We apply this result to a number of non-Hermitian random-matrix models and show that the outcome is in good agreement with numerical results.