  We consider the problem of structured canonical polyadic decomposition. If
the size of the problem is very big, then stochastic gradient approaches are
viable alternatives to classical methods, such as Alternating Optimization and
All-At-Once optimization. We extend a recent stochastic gradient approach by
employing an acceleration step (Nesterov momentum) in each iteration. We
compare our approach with state-of-the-art alternatives, using both synthetic
and real-world data, and find it to be very competitive.
