Truncation of Tensors in the Hierarchical Format
Contact the author: Please use for correspondence this email.
Submission date: 09. Apr. 2018
published in: SeMA journal : boletin Sociedad Espaõnola de la Matemática Aplicada (2019), pp not yet known
DOI number (of the published article): 10.1007/s40324-018-00184-5
MSC-Numbers: 15A69, 15A18, 15A99, 65F99, 65T99
Keywords and phrases: hierarchical tensor representation, HOSVD truncation
Download full preprint: PDF (494 kB)
Tensors are in general large-scale data which require a special representation. These representations are also called a format. After mentioning the r-term and tensor subspace formats, we describe the hierarchical tensor format which is the most ﬂexible one. Since operations with tensors often produce tensors of larger memory cost, truncation to reduced ranks is of utmost importance. The so-called higher-order singular-value decomposition (HOSVD) provides a save truncation with explicit error control. The paper explains in detail how the HOSVD procedure is performed within the hierarchical tensor format. Finally, we state special favourable properties of the HOSVD truncation.