Separation of variables for function generated high-order tensors
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Publication:474980
DOI10.1007/s10915-014-9822-4zbMath1307.65052OpenAlexW2095210482MaRDI QIDQ474980
Publication date: 25 November 2014
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10915-014-9822-4
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Uses Software
Cites Work
- Unnamed Item
- TT-cross approximation for multidimensional arrays
- Adaptive cross approximation of multivariate functions
- On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals
- Hierarchical matrices. A means to efficiently solve elliptic boundary value problems
- A theory of pseudoskeleton approximations
- Approximation of boundary element matrices
- An approach to n-mode components analysis
- Black box approximation of tensors in hierarchical Tucker format
- A new scheme for the tensor representation
- An equi-directional generalization of adaptive cross approximation for higher-order tensors
- Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
- Hierarchical Singular Value Decomposition of Tensors
- Breaking the Curse of Dimensionality, Or How to Use SVD in Many Dimensions
- Algorithm 862
- On the efficient computation of high-dimensional integrals and the approximation by exponential sums
- An Efficient Heuristic Procedure for Partitioning Graphs
- A Counterexample to the Possibility of an Extension of the Eckart--Young Low-Rank Approximation Theorem for the Orthogonal Rank Tensor Decomposition
- A Multilinear Singular Value Decomposition
- On the Best Rank-1 and Rank-(R1 ,R2 ,. . .,RN) Approximation of Higher-Order Tensors
- Sequential Unfolding SVD for Tensors With Applications in Array Signal Processing
- Tucker Dimensionality Reduction of Three-Dimensional Arrays in Linear Time
- Approximation of 1/x by exponential sums in [1, ∞)
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