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Inspired by the definition of tensor-tensor product and tensor tubal rank, a randomized singular value decomposition of tensor is presented in this paper. Based on tensor singular value decomposition (t-SVD) and tensor randomized singular value decomposition (t-RSVD), we obtain two efficient algorithms to solve tensor completion problem. We also propose the adaptive rank method to adjust the tubal rank of tensor. The main advantage of the random projection-based t-RSVD is to cut down the computing time in consideration of large-scale problems. In the optimization process, the alternating minimization algorithm is employed to solve the tensor completion problem. Finally, numerical experiment results indicate that the t-RSVD is competitive and consumes less time than the t-SVD. The efficiency and feasibility of our methods are illustrated by the image and video recovery. [ABSTRACT FROM AUTHOR]

This paper is concerned with the solution to the least squares problem for the quaternion Sylvester tensor equation. An iterative algorithm based on tensor format is presented to solve this problem. The convergence properties of the proposed iterative method are studied. We also consider the best approximate problem related to quaternion Sylvester tensor equation. Numerical examples are provided to confirm the theoretical results, which demonstrate that the proposed algorithm is effective and feasible for solving the least squares problem of the quaternion Sylvester tensor equation. [ABSTRACT FROM AUTHOR]

We propose a class of iterative algorithms to solve some tensor equations via Einstein product. These algorithms use tensor computations with no matricizations involved. For any (special) initial tensor, a solution (the minimal Frobenius norm solution) of related problems can be obtained within finite iteration steps in the absence of roundoff errors. Numerical examples are provided to confirm the theoretical results, which demonstrate that this kind of iterative methods are effective and feasible for solving some tensor equations. [ABSTRACT FROM AUTHOR]