  The Canonical Polyadic (CP) tensor decomposition is frequently used as a
model in applications in a variety of different fields. Using jackknife
resampling to estimate parameter uncertainties is often desirable but results
in an increase of the already high computational cost. Upon observation that
the resampled tensors, though different, are nearly identical, we show that it
is possible to extend the recently proposed Concurrent ALS (CALS) technique to
a jackknife resampling scenario. This extension gives access to the
computational efficiency advantage of CALS for the price of a modest increase
(typically a few percent) in the number of floating point operations. Numerical
experiments on both synthetic and real-world datasets demonstrate that the new
workflow based on a CALS extension can be several times faster than a
straightforward workflow where the jackknife submodels are processed
individually.
