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We present a sparse grid/hyperbolic cross discretization for many-particle problems.
It involves the tensor product of a one-particle multilevel basis. Subsequent truncation of the associated series expansion then results in a sparse grid discretization.
Here, depending on the norms involved, different variants of sparse grid techniques for many-particle spaces can be derived
that, in the best case, result in complexities and error estimates which are independent of the number of particles.
Furthermore...
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