Limiting Spectral Distributions of Sums of Products of Non-hermitian Random Matrices

For fixed l,m ≥ 1, let X n ,X n , . . . ,X n be independent random n × n matrices with independent entries, let F n := X n (X n )−1 · · · (X n )−1, and let F (1) n , . . . ,F (m) n be independent random matrices of the same form as F (0) n . We investigate the limiting spectral distributions of the matrices F (0) n and F (1) n +. . .+F (m) n as n→∞. Our main result shows that the sum F n +. . .+F n has the same limiting eigenvalue distribution as F (0) n after appropriate rescaling. This extends recent findings by Tikhomirov and Timushev (2014). Furthermore, we show that the limiting eigenvalue distribution of the matrices F (0) n is stable with respect to a suitably defined convolution ⊕. To obtain our results, we apply the general framework recently introduced in Götze, Kösters and Tikhomirov (2014) to sums of products of independent random matrices and their inverses. We establish the universality of the limiting singular value and eigenvalue distributions, and we provide a closer description of the limiting distributions in terms of free probability theory.