Proximal Alternating Linearized Minimization Algorithm for Sparse Tensor Train Decomposition

Authors

  • Zhenlong Hu Hangzhou Dianzi University
  • Zhongming Chen Hangzhou Dianzi University

DOI:

https://doi.org/10.19139/soic-2310-5070-2440

Abstract

 We address the sparse tensor train (TT) decomposition problem by incorporating an L1-norm regularization term. To improve numerical stability, orthogonality constraints are imposed on the problem. The tensor is expressed in the TT format, and the proximal alternating linearized minimization (PALM) algorithm is employed to solve the problem. Furthermore, we verify that the objective function qualifies as a Kurdyka-Lojasiewicz (KL) function and provide a convergence analysis. Numerical experiments on synthetic data and real data also demonstrate the efficiency of the proposed algorithm.

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Published

2025-03-10

How to Cite

Hu, Z., & Chen, Z. (2025). Proximal Alternating Linearized Minimization Algorithm for Sparse Tensor Train Decomposition. Statistics, Optimization & Information Computing, 13(5), 2119–2135. https://doi.org/10.19139/soic-2310-5070-2440

Issue

Vol. 13 No. 5 (2025)

Section

Research Articles

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