Relaxation of the rank-1 tensor approximation using different norms (Q6611979)

scientific article; zbMATH DE number 7919861

Language	Label	Description	Also known as
English	Relaxation of the rank-1 tensor approximation using different norms	scientific article; zbMATH DE number 7919861

Statements

instance of

scholarly article

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title

Relaxation of the rank-1 tensor approximation using different norms (English)

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author

Hassan Bozorgmanesh

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publication date

27 September 2024

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zbMATH Keywords

rank-1 approximation

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relaxation

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tensors

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maximum Z-eigenvalue

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Triangular decomposition of CP factors of a third-order tensor with application to solving nonlinear systems of equations

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On the reduction of multivariate quadratic systems to best rank-1 approximation of three-way tensors

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A Multilinear Singular Value Decomposition

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On the Best Rank-1 and Rank-(R1 ,R2 ,. . .,RN) Approximation of Higher-Order Tensors

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Nonlinear system modeling and identification using Volterra‐PARAFAC models

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On best rank one approximation of tensors

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Constrained Optimization with Low-Rank Tensors and Applications to Parametric Problems with PDEs

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Q3174157

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Convergence analysis of an SVD-based algorithm for the best rank-1 tensor approximation

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SVD-Based Algorithms for the Best Rank-1 Approximation of a Symmetric Tensor

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Conjugate gradient-like methods for solving general tensor equation with Einstein product

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Approximation algorithms for homogeneous polynomial optimization with quadratic constraints

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Most Tensor Problems Are NP-Hard

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An inexact augmented Lagrangian method for computing strongly orthogonal decompositions of tensors

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Best Nonnegative Rank-One Approximations of Tensors

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On the best rank-1 approximation of higher-order supersymmetric tensors

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Tensor Decompositions and Applications

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Tensor factorisation for narrowband single channel source decomposition

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Low-rank tensor completion by Riemannian optimization

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Bounds on the spectral norm and the nuclear norm of a tensor based on tensor partitions

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On Orthogonal Tensors and Best Rank-One Approximation Ratio

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A neural tensor decomposition model for high-order sparse data recovery

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Q4040780

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Semidefinite Relaxations for Best Rank-1 Tensor Approximations

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Adaptive nonlinear system identification. The Volterra and Wiener model approaches.

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A Low-Complexity Nonlinear Least Mean Squares Filter Based on a Decomposable Volterra Model

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Rank and eigenvalues of a supersymmetric tensor, the multivariate homogeneous polynomial and the algebraic hypersurface it defines

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Uniqueness of Nonnegative Tensor Approximations

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Tensor method for optimal control problems constrained by fractional three‐dimensional elliptic operator with variable coefficients

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Tensor Decomposition for Signal Processing and Machine Learning

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Common and individual structure of brain networks

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Parameter estimation for nonlinear Volterra systems by using the multi-innovation identification theory and tensor decomposition

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published in

ETNA - Electronic Transactions on Numerical Analysis

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Identifiers

zbMATH Open document ID

1548.651

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Mathematics Subject Classification ID

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10.1553/ETNA_VOL62S58

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Sitelinks

Mathematics(1 entry)

mardi Publication:6611979