## Numerical tensor calculus.(English)Zbl 1396.65091

Summary: The usual large-scale discretizations are applied to two or three spatial dimensions. The standard methods fail for higher dimensions because the data size increases exponentially with the dimension. In the case of a regular grid with $$n$$ grid points per direction, a spatial dimension $$d$$ yields $$n^d$$ grid points. A grid function defined on such a grid is an example of a tensor of order $$d$$. Here, suitable tensor formats help, since they try to approximate these huge objects by a much smaller number of parameters, which increases only linearly in $$d$$. In this way, data of size $$n^d = 1000^{1000}$$ can also be treated.
This paper introduces the algebraic and analytical aspects of tensor spaces. The main part concerns the numerical representation of tensors and the numerical performance of tensor operations.

### MSC:

 65F30 Other matrix algorithms (MSC2010) 15A69 Multilinear algebra, tensor calculus 65-02 Research exposition (monographs, survey articles) pertaining to numerical analysis

### Software:

N-way Toolbox; LOBPCG; cross2D; mftoolbox
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