  The Earth Mover's Distance is a popular similarity measure in several
branches of computer science. It measures the minimum total edge length of a
perfect matching between two point sets. The Earth Mover's Distance under
Translation ($\mathrm{EMDuT}$) is a translation-invariant version thereof. It
minimizes the Earth Mover's Distance over all translations of one point set.
  For $\mathrm{EMDuT}$ in $\mathbb{R}^1$, we present an
$\widetilde{\mathcal{O}}(n^2)$-time algorithm. We also show that this algorithm
is nearly optimal by presenting a matching conditional lower bound based on the
Orthogonal Vectors Hypothesis. For $\mathrm{EMDuT}$ in $\mathbb{R}^d$, we
present an $\widetilde{\mathcal{O}}(n^{2d+2})$-time algorithm for the $L_1$ and
$L_\infty$ metric. We show that this dependence on $d$ is asymptotically tight,
as an $n^{o(d)}$-time algorithm for $L_1$ or $L_\infty$ would contradict the
Exponential Time Hypothesis (ETH). Prior to our work, only approximation
algorithms were known for these problems.
