  The local convergence of alternating optimization methods with overrelaxation
for low-rank matrix and tensor problems is established. The analysis is based
on the linearization of the method which takes the form of an SOR iteration for
a positive semidefinite Hessian and can be studied in the corresponding
quotient geometry of equivalent low-rank representations. In the matrix case,
the optimal relaxation parameter for accelerating the local convergence can be
determined from the convergence rate of the standard method. This result relies
on a version of Young's SOR theorem for positive semidefinite $2 \times 2$
block systems.
