A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization

Technical Report · Thu Apr 28 04:00:00 EDT 2022

DOI:https://doi.org/10.2172/1865529· OSTI ID:1865529

Myers, Jeremy M. ^[1]; Dunlavy, Daniel M. ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

In this paper, we propose a hybrid method that uses stochastic and deterministic search to compute the maximum likelihood estimator of a low-rank count tensor with Poisson loss via state-of-theart local methods. Our approach is inspired by Simulated Annealing for global optimization and allows for fine-grain parameter tuning as well as adaptive updates to algorithm parameters. We present numerical results that indicate our hybrid approach can compute better approximations to the maximum likelihood estimator with less computation than the state-of-the-art methods by themselves.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: NA0003525
• OSTI ID: 1865529
• Report Number(s): SAND2022-5616R; 705644
• Country of Publication: United States
• Language: English

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