Generalized Perron-Frobenius theorem for multiple choice matrices, and applications

The celebrated Perron-Frobenius (PF) theorem is stated for irreducible nonnegative square matrices, and provides a simple characterization of their eigenvectors and eigenvalues. The importance of this theorem stems from the fact that eigenvalue problems on such matrices arise in many fields of science and engineering, including dynamical systems theory, economics, statistics and optimization. However, many real-life scenarios give rise to nonsquare matrices. Despite the extensive development of spectral theories for nonnegative matrices, the applicability of such theories to non-convex optimization problems is not clear. In particular, a natural question is whether the PF Theorem (along with its applications) can be generalized to a nonsquare setting. Our paper provides a generalization of the PF Theorem to nonsquare multiple choice matrices. The extension can be interpreted as representing systems with additional degrees of freedom, where each client entity may choose between multiple servers that can cooperate in serving it (while potentially interfering with other clients). This formulation is motivated by applications to power control in wireless networks, economics and others, all of which extend known examples for the use of the original PF Theorem. We show that the option of cooperation does not improve the situation, in the sense that in the optimum solution, no cooperation is needed, and only one server per client entity needs to work. Hence, the additional power of having several potential servers per client translates into choosing the "best" single server and not into sharing the load between the servers in some way, as one might have expected. The two main contributions of the paper are (i) a generalized PF Theorem that characterizes the optimal solution for a non-convex problem, and (ii) an algorithm for finding the optimal solution in polynomial time. In addition, we extend the definitions of irreducibility and largest eigenvalue of square matrices