Results 21 to 30 of about 156,394 (276)
A tensor norm approach to quantum compatibility [PDF]
Journal of Mathematical Physics, 2022 Measurement incompatibility is one of the most striking examples of how quantum physics is different from classical physics. Two measurements are incompatible if they cannot arise via classical post-processing from a third one. A natural way to quantify incompatibility is in terms of noise robustness. In the present article, we review recent results on
Andreas Bluhm, Ion Nechita
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Reshaped tensor nuclear norms for higher order tensor completion [PDF]
Machine Learning, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kishan Wimalawarne, Hiroshi Mamitsuka
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Tensor Completion Based on Triple Tubal Nuclear Norm
Algorithms, 2018 Many tasks in computer vision suffer from missing values in tensor data, i.e., multi-way data array. The recently proposed tensor tubal nuclear norm (TNN) has shown superiority in imputing missing values in 3D visual data, like color images and videos ...
Dongxu Wei +4 more
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Symmetric Tensor Decomposition by an Iterative Eigendecomposition Algorithm [PDF]
, 2016 We present an iterative algorithm, called the symmetric tensor eigen-rank-one iterative decomposition (STEROID), for decomposing a symmetric tensor into a real linear combination of symmetric rank-1 unit-norm outer factors using only eigendecompositions ...
Batselier, Kim, Wong, Ngai
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A concise proof to the spectral and nuclear norm bounds through tensor partitions
Open Mathematics, 2019 On estimations of the lower and upper bounds for the spectral and nuclear norm of a tensor, Li established neat bounds for the two norms based on regular tensor partitions, and proposed a conjecture for the same bounds to be hold based on general tensor ...
Kong Xu
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A parallel multi‐block alternating direction method of multipliers for tensor completion
IET Image Processing, 2021 This paper proposes an algorithm for the tensor completion problem of estimating multi‐linear data under the limitation of observation rate. Many tensor completion methods are based on nuclear norm minimization, they may fail to achieve the global ...
Hu Zhu +5 more
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Alternating Direction Method of Multipliers for Generalized Low-Rank Tensor Recovery
Algorithms, 2016 Low-Rank Tensor Recovery (LRTR), the higher order generalization of Low-Rank Matrix Recovery (LRMR), is especially suitable for analyzing multi-linear data with gross corruptions, outliers and missing values, and it attracts broad attention in the fields
Jiarong Shi +3 more
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Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms [PDF]
, 2015 Tensor rank and low-rank tensor decompositions have many applications in learning and complexity theory. Most known algorithms use unfoldings of tensors and can only handle rank up to $n^{\lfloor p/2 \rfloor}$ for a $p$-th order tensor in $\mathbb{R}^{n ...
Ge, Rong, Ma, Tengyu
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Vanishing theorems for higher-order Killing and Codazzi
Дифференциальная геометрия многообразий фигур, 2019 A Killing p-tensor (for an arbitrary natural number p ≥ 2) is a symmetric p-tensor with vanishing symmetrized covariant derivative. On the other hand, Codazzi p-tensor is a symmetric p-tensor with symmetric covariant derivative. Let M be a complete and
S. Stepanov, I. Tsyganok
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Some Generalized Versions of Chevet–Saphar Tensor Norms
Mathematics, 2022 The paper is concerned with some generalized versions gE and wE of classical tensor norms. We find a Banach space E for which gE and wE are finitely generated tensor norms, and show that gE and wE are associated with the ideals of some E-nuclear ...
Ju Myung Kim
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