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Quantum-entangled neuro-symbolic swarm federation for privacy-preserving IoMT-driven multimodal healthcare. [PDF]
Sci RepBen Othman S, Ali O.
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Diffuse interface model for two-phase flows on evolving surfaces with different densities: global well-posedness. [PDF]
Calc Var Partial Differ EquAbels H, Garcke H, Poiatti A.
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Notes on the Jellinek-Berry Thermostated Ideal Gas. [PDF]
J Stat PhysButler LT, Sharifi A.
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Tensor logarithmic norm and its applications
Numerical Linear Algebra with Applications, 2016SummaryMatrix logarithmic norm is an important quantity, which characterize the stability of linear dynamical systems. We propose the logarithmic norms for tensors and tensor pairs, and extend some classical results from the matrix case. Moreover, the explicit forms of several tensor logarithmic norms and semi‐norms are also derived.
Ding, Weiyang, Hou, Zongyuan, Wei, Yimin
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Logarithmic Norms for Matrix Pencils
SIAM Journal on Matrix Analysis and Applications, 1999Summary: The author extends the usual concepts of least upper bound norm and logarithmic norm of a matrix to matrix pencils. Properties of these seminorms and logarithmic norms are derived. This logarithmic norm can be used to study the growth of the solutions to linear variable coefficient differential-algebraic systems.
Higueras, Inmaculada +1 more
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Logarithmic Norm Regularized Low-Rank Factorization for Matrix and Tensor Completion
IEEE Transactions on Image Processing, 2021Matrix and tensor completion aim to recover the incomplete two- and higher-dimensional observations using the low-rank property. Conventional techniques usually minimize the convex surrogate of rank (such as the nuclear norm), which, however, leads to the suboptimal solution for the low-rank recovery.
Lin Chen +3 more
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Logarithmic Schatten-p Norm Minimization for Tensorial Multi-view Subspace Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022The low-rank tensor could characterize inner structure and explore high-order correlation among multi-view representations, which has been widely used in multi-view clustering. Existing approaches adopt the tensor nuclear norm (TNN) as a convex approximation of non-convex tensor rank function.
Jipeng, Guo +4 more
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