We prove that a popular class of methods for the Tensor Rank Decomposition problem (called «Pencil Based Algorithms») are unstable, in the sense that the error of the solution is much larger than expected from the condition number of the input.

There is a simple geometrical reason for this: the first step of the algorithm is a projection to a lower–dimensional tensor… that turns out to be ill–conditioned with much higher probability!