$M$-QR decomposition and hyperpower iterative methods for computing outer inverses of tensors
<|reference_start|>$M$-QR decomposition and hyperpower iterative methods for computing outer inverses of tensors: The outer inverse of tensors plays increasingly significant roles in computational mathematics, numerical analysis, and other generalized inverses of tensors. In this paper, we compute outer inverses with prescribed ranges and kernels of a given tensor through tensor QR decomposition and hyperpower iterative method under the M-product structure, which is a family of tensor-tensor products, generalization of the t-product and c-product, allows us to suit the physical interpretations across those different modes. We discuss a theoretical analysis of the nineteen-order convergence of the proposed tensor-based iterative method. Further, we design effective tensor-based algorithms for computing outer inverses using M-QR decomposition and hyperpower iterative method. The theoretical results are validated with numerical examples demonstrating the appropriateness of the proposed methods.<|reference_end|>
@article{behera2024$m$-qr,
    title={$M$-QR decomposition and hyperpower iterative methods for computing
  outer inverses of tensors},
    author={Ratikanta Behera, Krushnachandra Panigrahy, Jajati Keshari Sahoo,
  Yimin Wei},
    journal={arXiv preprint arXiv:2409.07007},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07007},
    primaryClass={math.NA cs.NA}
}