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MiS Preprint

Hierarchical Singular Value Decomposition of Tensors

Lars Grasedyck


We define the hierarchical singular value decomposition (SVD) for tensors of order $d\ge2$. This hierarchical SVD has properties like the matrix SVD (and collapses to the SVD in $d=2$), and we prove these.

In particular, one can find low rank (almost) best approximations in a hierarchical format (${\cal H}$-Tucker) which requires only ${\cal O}((d-1)k^3+dnk)$ data, where $d$ is the order of the tensor, $n$ the size of the modes and $k$ the rank. The ${\cal H}$-Tucker format is a specialization of the Tucker format and it contains as a special case all (canonical) rank $k$ tensors. Based on this new concept of a hierarchical SVD we present algorithms for hierarchical tensor calculations allowing for a rigorous error analysis. The complexity of the truncation (finding lower rank approximations to hierarchical rank $k$ tensors) is in ${\cal O}((d-1)k^4+dnk^2)$ and the attainable accuracy is just 2--3 digits less than machine precision.

SVD, Tucker, Tensor

Related publications

2010 Repository Open Access
Lars Grasedyck

Hierarchical singular value decomposition of tensors

In: SIAM journal on matrix analysis and applications, 31 (2010) 4, pp. 2029-2054