An All–at–Once CP Decomposition Method for Count Tensors

Ranadive, Teresa; Baskaran, Muthu Manikandan

doi:10.1109/hpec49654.2021.9622828

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Cited by 3 publications

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“…Alternating least squares methods are relatively easy to implement and effective when used with LASSOtype regularization [12,23]. The method of Ranadive et al [55], CP-POPT-DGN, is an allat-once active set trust-region gradient-projection method. CP-POPT-DGN is functionally very similar to CPAPR-PDN.…”

Section: 4mentioning

confidence: 99%

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

Myers¹,

Dunlavy²

2022

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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.

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Section: 4mentioning

confidence: 99%