  In this work we generalize the Jacobi-Davidson method to the case when
eigenvector can be reshaped into a low-rank matrix. In this setting the
proposed method inherits advantages of the original Jacobi-Davidson method, has
lower complexity and requires less storage. We also introduce low-rank version
of the Rayleigh quotient iteration which naturally arises in the
Jacobi-Davidson method.
