Randomized density matrix renormalization group algorithm for low-rank tensor train decomposition

  • Huilin Jiang
  • Zhongming Chen Hangzhou Dianzi University
  • Gaohang Yu
Keywords: tensor train decomposition, randomized algorithm, proximal regularization, TensorSketch, DMRG


Tensor train decomposition is a powerful tool for processing high-dimensional data. Density matrix renormalization group (DMRG) is an alternating scheme for low-rank tensor train decomposition of large tensors. However, it may suffer from the curse of dimensionality due to the large scale of subproblems. In this paper, we proposed a novel randomized proximal DMRG algorithm for low-rank tensor train decomposition by using TensorSketch to alleviate the curse of dimensionality. Numerical experiments on synthetic and real-world data also demonstrate the effectiveness and efficiency of the proposed algorithm.


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How to Cite
Jiang, H., Chen, Z., & Yu, G. (2024). Randomized density matrix renormalization group algorithm for low-rank tensor train decomposition. Statistics, Optimization & Information Computing, 12(4), 1061-1075. https://doi.org/10.19139/soic-2310-5070-2030
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