Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion

Rui Liu, Alex Olshevsky

Abstract: We study alternating minimization for matrix completion in the simplest possible setting: completing a rank-one matrix from a revealed subset of the entries. We bound the asymptotic convergence rate by the variational characterization of eigenvalues of a reversible consensus problem. This leads to a polynomial upper bound on the asymptotic rate in terms of number of nodes as well as the largest degree of the graph of revealed entries.