  Tensor decomposition models play an increasingly important role in modern
data science applications. One problem of particular interest is fitting a
low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has
sparse structure and the tensor elements are nonnegative count data. SparTen is
a high-performance C++ library which computes a low-rank decomposition using
different solvers: a first-order quasi-Newton or a second-order damped Newton
method, along with the appropriate choice of runtime parameters. Since default
parameters in SparTen are tuned to experimental results in prior published work
on a single real-world dataset conducted using MATLAB implementations of these
methods, it remains unclear if the parameter defaults in SparTen are
appropriate for general tensor data. Furthermore, it is unknown how sensitive
algorithm convergence is to changes in the input parameter values. This report
addresses these unresolved issues with large-scale experimentation on three
benchmark tensor data sets. Experiments were conducted on several different CPU
architectures and replicated with many initial states to establish generalized
profiles of algorithm convergence behavior.
