A stochastic algorithm for the ParaTuck decomposition

This paper introduces a novel stochastic algorithm for the ParaTuck Decomposition (PTD), addressing the challenge of local minima encountered in the traditional alternating least squares (ALS) approach. The proposed method integrates stochastic steps into the ALS framework to avoid the common swamp problems, where numerical difficulties prevent accurate decompositions. Our simulations indicate good convergence properties for PTD, suggesting a potential increase in the efficiency and reliability of this tensor decomposition across various applications.