AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This paper proposes a randomized tensor QLP decomposition (rt-QLP) for third-order tensors without pivoting, extending the matrix-based QLP to the tensor setting in the transformed domain.
This work develops TTD- and HTD-based formulations for the T-product and its associated key algebra by operating directly on the factor matrices or tensors of the decompositions of the decompositions.
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.
Sampling a multidomain tensor from limited measurements is fundamental in structured linear inverse problems. Kronecker-structured sampling avoids the full high-dimensional sensing matrix, but design remains difficult: sequential discrete methods can commit the cross-mode budget too early, whereas standard continuous g...
Tensor singular value decomposition (T-SVD), which is built upon the tensor-tensor product (t-product), has emerged as a powerful tool for processing high-dimensional visual data such as color images and videos. However, the standard t-product imposes strict dimensional compatibility constraints. Although extensions ba...
A structure-regularized CP (SR-CP) framework that uses a unified quadratic penalty to encode diverse structural priors through positive semidefinite matrices and a soft orthogonality-promoting penalty is further introduced to enhance component distinctiveness and numerical stability.
Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise ev...
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