R3MC: A Riemannian three-factor algorithm for low-rank matrix completion

We exploit the versatile framework of Riemannian optimization on quotient manifolds to implement R3MC, a nonlinear conjugate gradient method optimized for matrix completion. The underlying geometry uses a Riemannian metric tailored to the quadratic cost. Numerical comparisons suggest that R3MC robustly outperforms state-of-the-art algorithms across different synthetic and real datasets especially in instances that combine scarcely sampled and ill-conditioned data.