# Local convergence of alternating low-rank optimization methods with overrelaxation

@article{Oseledets2021LocalCO, title={Local convergence of alternating low-rank optimization methods with overrelaxation}, author={I. Oseledets and Maxim V. Rakhuba and Andr{\'e} Uschmajew}, journal={ArXiv}, year={2021}, volume={abs/2111.14758} }

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…

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