Fast Multidimensional Convolution in Low-Rank Tensor Formats via Cross Approximation
SIAM JOURNAL ON SCIENTIFIC COMPUTING(2015)
摘要
We propose a new cross-conv algorithm for approximate computation of convolution in different low-rank tensor formats (tensor train, Tucker, hierarchical Tucker). It has better complexity with respect to the tensor rank than previous approaches. The new algorithm has a high potential impact in different applications. The key idea is based on applying cross approximation in the "frequency domain," where convolution becomes a simple elementwise product. We illustrate efficiency of our algorithm by computing the three-dimensional Newton potential and by presenting preliminary results for solution of the Hartree-Fock equation on tensor-product grids.
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关键词
multidimensional convolution,tensor train,tensor decompositions,multilinear algebra,cross approximation,black box approximation
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