  Low-rank inductive matrix completion (IMC) is currently widely used in IoT
data completion, recommendation systems, and so on, as the side information in
IMC has demonstrated great potential in reducing sample point remains a major
obstacle for the convergence of the nonconvex solutions to IMC. What's more,
carefully choosing the initial solution alone does not usually help remove the
saddle points. To address this problem, we propose a stocastic variance
reduction gradient-based algorithm called LRSVRG-IMC. LRSVRG-IMC can escape
from the saddle points under various low-rank and sparse conditions with a
properly chosen initial input. We also prove that LRSVVRG-IMC achieves both a
linear convergence rate and a near-optimal sample complexity. The superiority
and applicability of LRSVRG-IMC are verified via experiments on synthetic
datasets.
