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Two randomized algorithms within the sketched GMRES framework that replace full Arnoldi orthogonalization with short recurrences are proposed, providing robustness across a wide range of problems and outperform standard low-rank Tucker solvers in symmetric and non-symmetric settings.
This work presents the tree tensor network Nyström (TTNN), an algorithm that extends recent research on streamable tensor approximation to the more general tree tensor network format, enabling a unified treatment of various existing methods.
In this work, we present the tree tensor network Nyström (TTNN), an algorithm that extends recent research on streamable tensor approximation, such as for Tucker and tensor‐train formats, to the more general tree tensor network format, enabling a unified treatment of various existing methods. Our method retains the key features of the generalized Nyström approximation for matrices, that is, randomized, single‐pass, streamable, and cost‐effective. Additionally, the structure of the sketches allows for parallel implementation. We provide a deterministic error bound for the algorithm and, in the specific case of Gaussian dimension reduction maps, also a probabilistic one. We also introduce a sequential variant of the algorithm, referred to as sequential tree tensor network Nyström (STTNN), which offers better performance for dense tensors. Furthermore, both algorithms are well‐suited for the recompression or rounding of tensors in the tree tensor network format. Numerical experiments highlight the efficiency and effectiveness of the proposed methods.