[1] Bai, Z.-Z., Pan, J.-Y.: Matrix Analysis and Computations. Society for Industrial and Applied Mathematics, Philadelphia, PA (2021) [2] Bai, Z.-Z., Wang, L., Wu, W.-T.: On convergence rate of the randomized Gauss-Seidel method. Linear Algebra Appl. 611, 237-252 (2021) [3] Bai, Z.-Z., Wu, W.-T.: On greedy randomized Kaczmarz method for solving large sparse linear systems. Soc. Indus. Appl. Math. J. Sci. Comput. 40(1), A592-A606 (2018) [4] Bai, Z.-Z., Wu, W.-T.: On relaxed greedy randomized Kaczmarz methods for solving large sparse linear systems. Appl. Math. Lett. 83, 21-26 (2018) [5] Bai, Z.-Z., Wu, W.-T.: On greedy randomized coordinate descent methods for solving large linear least-squares problems. Numer. Linear Alg. Appl. 26(4), 1-15 (2019) [6] Bai, Z.-Z., Wu, W.-T.: Randomized Kaczmarz iteration methods: algorithmic extensions and convergence theory. Jpn J. Indus. Appl. Math. 40, 1421-1443 (2023) [7] Bottou, L.: Stochastic gradient descent tricks. In: Neural Networks: Tricks of the Trade. 2nd edn. Springer, Berlin, Heidelberg (2012) [8] Cates, J., Hoover, R.C., Caudle, K., Kopp, R., Ozdemir, C.: Transform-based tensor auto regression for multilinear time series forecasting. In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE. 461-466 (2021) [9] Cates, J., Hoover, R.C., Caudle, K., Ozdemir, C., Braman, K., Machette, D.: Forecasting multilinear data via transform-based tensor autoregression. arXiv:2205.12201 (2022) [10] Charikar, M., Chen, K., Farach-Colton, M.: Finding frequent items in data streams. In: International Colloquium on Automata, Languages, and Programming. pp. 693-703 Springer, Berlin, Heidelberg. pp (2002) [11] Cooley, J.W., Tukey, J.W.: An algorithm for the machine calculation of complex Fourier series. Math. Comput. 19(90), 297-301 (1965) [12] Deng, J.-W., Deng, J.-L., Yin, D., Jiang, R.-H., Song, X.: TTS-norm: forecasting tensor time series via multi-way normalization. Asso. Comput. Mach. Trans. Knowl. Discov. Data. 18(1), 1-25 (2023) [13] Gazagnadou, N., Ibrahim, M., Gower, R.M.: RidgeSketch: a fast sketching based solver for large scale ridge regression. SIAM J. Matrix Anal. Appl. 43(3), 1440-1468 (2022) [14] Gower, R.M., Richtárik, P.: Randomized iterative methods for linear systems. SIAM J. Matrix Anal. Appl. 36(4), 1660-1690 (2015) [15] Hill, C., Li, J., Schneider, M.J., Wells, M.T.: The tensor auto-regressive model. J Forecast. 40(4), 636-652 (2021) [16] Indyk, P., Vakilian, A., Yuan, Y.: Learning-based low-rank approximations. arXiv:1910.13984 (2019) [17] Kernfeld, E., Kilmer, M., Aeron, S.: Tensor-tensor products with invertible linear transforms. Linear Algebra Appl. 485, 545-570 (2015) [18] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: a theoretical and computational framework with applications in imaging. SIAM J. Matrix Anal. Appl. 34(1), 148-172 (2013) [19] Kilmer, M.E., Martin, C.D.: Factorization strategies for third-order tensors. Linear Algebra Appl. 435(3), 641-658 (2011) [20] Leventhal, D., Lewis, A.S.: Randomized methods for linear constraints: convergence rates and conditioning. Math. Oper. Res. 35(3), 641-654 (2010) [21] Li, Z., Xiao, H.: Multi-linear tensor autoregressive models. arXiv:2110.00928 (2021) [22] Liu, S., Liu, T., Vakilian, A., Wan, Y., Woodruff, D.P.: On learned sketches for randomized numerical linear algebra. arXiv:2007.09890 (2020) [23] Liu, Y., Liu, J., Long, Z., Zhu, C.: Tensor Computation for Data Analysis. Springer, Switzerland (2022) [24] Pagh, R.: Compressed matrix multiplication. Assoc. Comput. Mach. Trans. Comput. Theo. 5(3), 1-17 (2013) [25] Pham, N., Pagh, R.: Fast and scalable polynomial kernels via explicit feature maps. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp.239-247 (2013) [26] Saad, Y.: Iterative Methods for Sparse Linear Systems, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia (2003) [27] Shi, Y., Anandkumar, A.: Higher-order count sketch: dimensionality reduction that retains efficient tensor operations. arXiv:1901.11261 (2019) [28] Stich, S.U., Muller, C.L., Gartner, B.: Optimization of convex functions with random pursuit. SIAM J. Opt. 23(2), 1284-1309 (2013) [29] Strohmer, T., Vershynin, R.: A randomized Kaczmarz algorithm with exponential convergence. J. Fourier Anal. Appl. 15(2), 262-278 (2009) [30] Tang, L., Yu, Y., Zhang, Y., Li, H.: Sketch-and-project methods for tensor linear systems. Numer. Linear Algebra Appl. 30(2), e2470 (2023) [31] Wang, D., Zheng, Y., Li, G.: High-dimensional low-rank tensor autoregressive time series modeling. J. Econ. 238(1), 105544 (2023) [32] Zhang, J.-H., Guo, J.-H.: On relaxed greedy randomized coordinate descent methods for solving large linear least-squares problems. Appl. Numer. Math. 157, 372-384 (2020) |