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Based on sketching techniques, we propose two practical randomized algorithms for tensor ring (TR) decomposition. Specifically, on the basis of defining new tensor products and investigating their properties, the two algorithms are devised by applying the Kronecker sub‐sampled randomized Fourier transform and TensorSketch to the alternating least squares subproblems derived from the minimization problem of TR decomposition. From the former, we find an algorithmic framework based on random projection for randomized TR decomposition. We compare our proposals with the existing methods using both synthetic and real data. Numerical results show that they have quite decent performance in accuracy and computing time. [ABSTRACT FROM AUTHOR]

Tensor ring (TR) decomposition has been widely applied as an effective approach in a variety of applications to discover the hidden low‐rank patterns in multidimensional and higher‐order data. A well‐known method for TR decomposition is the alternating least squares (ALS). However, solving the ALS subproblems often suffers from high cost issue, especially for large‐scale tensors. In this paper, we provide two strategies to tackle this issue and design three ALS‐based algorithms. Specifically, the first strategy is used to simplify the calculation of the coefficient matrices of the normal equations for the ALS subproblems, which takes full advantage of the structure of the coefficient matrices of the subproblems and hence makes the corresponding algorithm perform much better than the regular ALS method in terms of computing time. The second strategy is to stabilize the ALS subproblems by QR factorizations on TR‐cores, and hence the corresponding algorithms are more numerically stable compared with our first algorithm. Extensive numerical experiments on synthetic and real data are given to illustrate and confirm the above results. In addition, we also present the complexity analyses of the proposed algorithms. [ABSTRACT FROM AUTHOR]

For tensor linear systems with respect to the popular t‐product, we first present the sketch‐and‐project method and its adaptive variants. Their Fourier domain versions are also investigated. Then, considering that the existing sketching tensor or way for sampling has some limitations, we propose two improved strategies. Convergence analyses for the methods mentioned above are provided. We compare our methods with the existing ones using synthetic and real data. Numerical results show that they have quite decent performance in terms of the number of iterations and running time. [ABSTRACT FROM AUTHOR]