  Tensor factorizations with nonnegative constraints have found application in
analyzing data from cyber traffic, social networks, and other areas. We
consider application data best described as being generated by a Poisson
process (e.g., count data), which leads to sparse tensors that can be modeled
by sparse factor matrices. In this paper we investigate efficient techniques
for computing an appropriate canonical polyadic tensor factorization based on
the Kullback-Leibler divergence function. We propose novel subproblem solvers
within the standard alternating block variable approach. Our new methods
exploit structure and reformulate the optimization problem as small independent
subproblems. We employ bound-constrained Newton and quasi-Newton methods. We
compare our algorithms against other codes, demonstrating superior speed for
high accuracy results and the ability to quickly find sparse solutions.
