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Tensor decomposition models play an increasingly important role in moderndata science applications. One problem of particular interest is fitting alow-rank Canonical Polyadic (CP) tensor decomposition model when the tensor hassparse structure and the tensor elements are nonnegative count data. SparTen isa high-performance C++ library which computes a low-rank decomposition usingdifferent solvers: a first-order quasi-Newton or a second-order damped Newtonmethod, along with the appropriate choice of runtime parameters. Since defaultparameters in SparTen are tuned to experimental results in prior published workon a single real-world dataset conducted using MATLAB implementations of thesemethods, it remains unclear if the parameter defaults in SparTen areappropriate for general tensor data. Furthermore, it is unknown how sensitivealgorithm convergence is to changes in the input parameter values. This reportaddresses these unresolved issues with large-scale experimentation on threebenchmark tensor data sets. Experiments were conducted on several different CPUarchitectures and replicated with many initial states to establish generalizedprofiles of algorithm convergence behavior.

Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software: Extended Analysis