RA-HOOI: rank-adaptive higher-order orthogonal iteration for the fixed-accuracy low multilinear-rank approximation of tensors
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Publication:6549553
DOI10.1016/j.apnum.2024.03.004zbMath1545.65202MaRDI QIDQ6549553
Publication date: 4 June 2024
Published in: Applied Numerical Mathematics (Search for Journal in Brave)
tensorfixed-accuracyhigher-order orthogonal iterationlow multilinear-rank approximationrank-adaptive strategy
Multilinear algebra, tensor calculus (15A69) Numerical linear algebra (65F99) Numerical methods for low-rank matrix approximation; matrix compression (65F55)
Cites Work
- Unnamed Item
- Tensor Decompositions and Applications
- The density-matrix renormalization group in the age of matrix product states
- Handwritten digit classification using higher order singular value decomposition
- Multidimensional filtering based on a tensor approach
- Differential-geometric Newton method for the best rank-\((R _{1}, R _{2}, R _{3})\) approximation of tensors
- Dimensionality reduction in higher-order signal processing and rank-\((R_1,R_2,\ldots,R_N)\) reduction in multilinear algebra
- The approximation of one matrix by another of lower rank.
- Randomized algorithms for the approximations of Tucker and the tensor train decompositions
- Parallel ALS Algorithm for Solving Linear Systems in the Hierarchical Tucker Representation
- A Randomized Blocked Algorithm for Efficiently Computing Rank-revealing Factorizations of Matrices
- A New Truncation Strategy for the Higher-Order Singular Value Decomposition
- Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
- Best Low Multilinear Rank Approximation of Higher-Order Tensors, Based on the Riemannian Trust-Region Scheme
- Randomized algorithms for the low-rank approximation of matrices
- Randomized Algorithms for Matrices and Data
- Low-Rank Approximation and Regression in Input Sparsity Time
- A Newton–Grassmann Method for Computing the Best Multilinear Rank-$(r_1,$ $r_2,$ $r_3)$ Approximation of a Tensor
- A Multilinear Singular Value Decomposition
- On the Best Rank-1 and Rank-(R1 ,R2 ,. . .,RN) Approximation of Higher-Order Tensors
- Efficient Randomized Algorithms for the Fixed-Precision Low-Rank Matrix Approximation
- Numerical tensor calculus
- Randomized Algorithms for Low-Rank Tensor Decompositions in the Tucker Format
- TuckerMPI
- Randomized Projection for Rank-Revealing Matrix Factorizations and Low-Rank Approximations
- Adaptive Hierarchical Subtensor Partitioning for Tensor Compression
- Numerical linear algebra in the streaming model
- Quasi-Newton Methods on Grassmannians and Multilinear Approximations of Tensors
- Accurate Low-Rank Approximations Via a Few Iterations of Alternating Least Squares
- Randomized numerical linear algebra: Foundations and algorithms
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