L^{\infty } estimation of tensor truncations

Tensor truncation techniques are based on singular value decompositions. Therefore, the direct error control is restricted to $\ell ^{2}$ or $L^{2}$ norms. On the other hand, one wants to approximate multivariate (grid) functions in appropriate tensor formats in order to perform cheap pointwise evaluations, which require $\ell ^{\infty }$ or $L^{\infty }$ error estimates. Due to the huge dimensions of the tensor spaces, a direct estimate of $\left\| \cdot \right\| _{\infty }$ by $\left\| \cdot \right\| _{2}$ is hopeless. In the paper we prove that, nevertheless, in cases where the function to be approximated is smooth, reasonable error estimates with respect to $\left\| \cdot \right\| _{\infty }$ can be derived from the Gagliardo–Nirenberg inequality because of the special nature of the singular value decomposition truncation.