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- W2022917013 abstract "In recent papers the tensorisation of vectors has been discussed. In principle, this is the isomorphic representation of an $${mathbb{R}^{n}}$$ vector as a tensor. Black-box tensor approximation methods can be used to reduce the data size of the tensor representation. In particular, if the vector corresponds to a grid function, the resulting data size can become much smaller than n, e.g., $${O(log n)ll n}$$. In this article we discuss the convolution of two vectors which are given via a sparse tensor representation. We want to obtain the result again in the tensor representation. Furthermore, the cost of the convolution algorithm should be related to the operands’ data sizes. While $${mathbb{R}^{n}}$$ vectors can be considered as grid values of function, we also apply the corresponding procedure to univariate functions." @default.
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- W2022917013 date "2011-07-14" @default.
- W2022917013 modified "2023-10-18" @default.
- W2022917013 title "Tensorisation of vectors and their efficient convolution" @default.
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- W2022917013 doi "https://doi.org/10.1007/s00211-011-0393-0" @default.
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