The $L\log L$-Luxemburg Discrepancy for Nonnegative Weights Suffers from the Curse of Dimensionality

We study the normalized discrepancy in the Luxemburg norm generated by $\mathrm{\Phi }(s)=s\log (1+s)$. For arbitrary nonnegative quadrature weights, we prove that the associated information complexity suffers from the curse of dimensionality: for every $\epsilon \in (0,1)$, there exist constants $c_{\epsilon }>0$ and $d_0=d_0(\epsilon )\in \mathbb{N}$ such that $$N_{\mathrm{\Phi },+}(\epsilon ,d)\ge c_{\epsilon }{d}^{-1}(1.07735\dots {)}^{d}foralld\ge d_0\mathrm{.}$$ The result therefore also applies to equal-weight quasi-Monte Carlo rules.