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arXiv · 1507.04721

On Accelerating the Regularized Alternating Least Square Algorithm for Tensors

Xiaofei Wang·Carmeliza Navasca·Stefan Kindermann
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Abstract

In this paper, we discuss the acceleration of the regularized alternating least square (RALS) algorithm for tensor approximation. We propose a fast iterative method using a Aitken-Stefensen like updates for the regularized algorithm. Through numerical experiments, the fast algorithm demonstrate a faster convergence rate for the accelerated version in comparison to both the standard and regularized alternating least squares algorithms. In addition, we analyze the global convergence based on the Kurdyka- Lojasiewicz inequality as well as show that the RALS algorithm has a linear local convergence rate.

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Xiaofei Wang, Carmeliza Navasca, Stefan Kindermann. 2015-07-16. On Accelerating the Regularized Alternating Least Square Algorithm for Tensors. https://arxiv.org/abs/1507.04721

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