Randomized algorithms for the computation of multilinear rank-(μ1,μ2,μ3) approximations

We present some randomized algorithms for computing multilinear rank- ( μ 1 , μ 2 , μ 3 ) approximations of tensors by combining the sparse subspace embedding and the singular value decomposition. The error bound for this algorithm with the high probability is obtained by the properties of sparse su...

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Vydáno v:Journal of global optimization Ročník 87; číslo 2-4; s. 373 - 403
Hlavní autoři: Che, Maolin, Wei, Yimin, Xu, Yanwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.11.2023
Springer
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ISSN:0925-5001, 1573-2916
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Abstract We present some randomized algorithms for computing multilinear rank- ( μ 1 , μ 2 , μ 3 ) approximations of tensors by combining the sparse subspace embedding and the singular value decomposition. The error bound for this algorithm with the high probability is obtained by the properties of sparse subspace embedding. Furthermore, combining the power scheme and the proposed randomized algorithm, we derive a three-stage randomized algorithm and make a probabilistic analysis for its error bound. The efficiency of the proposed algorithms is illustrated via numerical examples.
AbstractList We present some randomized algorithms for computing multilinear rank- ( μ 1 , μ 2 , μ 3 ) approximations of tensors by combining the sparse subspace embedding and the singular value decomposition. The error bound for this algorithm with the high probability is obtained by the properties of sparse subspace embedding. Furthermore, combining the power scheme and the proposed randomized algorithm, we derive a three-stage randomized algorithm and make a probabilistic analysis for its error bound. The efficiency of the proposed algorithms is illustrated via numerical examples.
We present some randomized algorithms for computing multilinear rank- [Formula omitted] approximations of tensors by combining the sparse subspace embedding and the singular value decomposition. The error bound for this algorithm with the high probability is obtained by the properties of sparse subspace embedding. Furthermore, combining the power scheme and the proposed randomized algorithm, we derive a three-stage randomized algorithm and make a probabilistic analysis for its error bound. The efficiency of the proposed algorithms is illustrated via numerical examples.
Audience Academic
Author Xu, Yanwei
Che, Maolin
Wei, Yimin
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  organization: Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co., Ltd
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CitedBy_id crossref_primary_10_1109_TIP_2024_3385284
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crossref_primary_10_1007_s10915_022_01956_y
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Issue 2-4
Keywords approximation
Multilinear rank
65F10
Singular values
Randomized algorithms
Sparse subspace embedding
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Random projection
The power scheme
Singular value decomposition
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PublicationSubtitle An International Journal Dealing with Theoretical and Computational Aspects of Seeking Global Optima and Their Applications in Science, Management and Engineering
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Snippet We present some randomized algorithms for computing multilinear rank- ( μ 1 , μ 2 , μ 3 ) approximations of tensors by combining the sparse subspace embedding...
We present some randomized algorithms for computing multilinear rank- [Formula omitted] approximations of tensors by combining the sparse subspace embedding...
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SubjectTerms Algorithms
Analysis
Computer Science
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Real Functions
Title Randomized algorithms for the computation of multilinear rank-(μ1,μ2,μ3) approximations
URI https://link.springer.com/article/10.1007/s10898-022-01182-8
Volume 87
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