The Epsilon-Alternating Least Squares for Orthogonal Low-Rank Tensor Approximation and Its Global Convergence
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Publication:5146632
DOI10.1137/19M1303113zbMath1458.90528arXiv1911.10921OpenAlexW3100052556MaRDI QIDQ5146632
Publication date: 26 January 2021
Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1911.10921
Nonconvex programming, global optimization (90C26) Best approximation, Chebyshev systems (41A50) Eigenvalues, singular values, and eigenvectors (15A18) Multilinear algebra, tensor calculus (15A69)
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On approximation algorithm for orthogonal low-rank tensor approximation ⋮ Linear convergence of an alternating polar decomposition method for low rank orthogonal tensor approximations ⋮ Half-quadratic alternating direction method of multipliers for robust orthogonal tensor approximation ⋮ Jacobi-type algorithms for homogeneous polynomial optimization on Stiefel manifolds with applications to tensor approximations ⋮ Rank properties and computational methods for orthogonal tensor decompositions ⋮ Shifted eigenvalue decomposition method for computing C-eigenvalues of a piezoelectric-type tensor
Uses Software
Cites Work
- Unnamed Item
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- Tensor Decompositions and Applications
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- Approximation algorithms for homogeneous polynomial optimization with quadratic constraints
- Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics
- Independent component analysis, a new concept?
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
- Orthogonal Tensor Decompositions
- On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors
- Semialgebraic Geometry of Nonnegative Tensor Rank
- Jacobi Algorithm for the Best Low Multilinear Rank Approximation of Symmetric Tensors
- A New Truncation Strategy for the Higher-Order Singular Value Decomposition
- A new convergence proof for the higher-order power method and generalizations
- Hierarchical Singular Value Decomposition of Tensors
- Globally Convergent Jacobi-Type Algorithms for Simultaneous Orthogonal Symmetric Tensor Diagonalization
- Shifted Power Method for Computing Tensor Eigenpairs
- Orthogonal Low Rank Tensor Approximation: Alternating Least Squares Method and Its Global Convergence
- On the Tensor SVD and the Optimal Low Rank Orthogonal Approximation of Tensors
- Computing the Polar Decomposition—with Applications
- A Multilinear Singular Value Decomposition
- On the Best Rank-1 and Rank-(R1 ,R2 ,. . .,RN) Approximation of Higher-Order Tensors
- Tensor Decomposition for Signal Processing and Machine Learning
- Canonical Polyadic Decomposition with a Columnwise Orthonormal Factor Matrix
- Quasi-Newton Methods on Grassmannians and Multilinear Approximations of Tensors
- Numerical Computation for Orthogonal Low-Rank Approximation of Tensors
- Blind Multilinear Identification
- Most Tensor Problems Are NP-Hard
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