  Computing Wasserstein barycenters of discrete measures has recently attracted
considerable attention due to its wide variety of applications in data science.
In general, this problem is NP-hard, calling for practical approximative
algorithms. In this paper, we analyze a well-known simple framework for
approximating Wasserstein-$p$ barycenters, where we mainly consider the most
common case $p=2$ and $p=1$, which is not as well discussed. The framework
produces sparse support solutions and shows good numerical results in the
free-support setting. Depending on the desired level of accuracy, this requires
only $N-1$ or $N(N-1)/2$ standard two-marginal optimal transport (OT)
computations between the $N$ input measures, respectively, which is fast,
memory-efficient and easy to implement using any OT solver as a black box. What
is more, these methods yield a relative error of at most $N$ and $2$,
respectively, for both $p=1, 2$. We show that these bounds are practically
sharp. In light of the hardness of the problem, it is not surprising that such
guarantees cannot be close to optimality in general. Nevertheless, these error
bounds usually turn out to be drastically lower for a given particular problem
in practice and can be evaluated with almost no computational overhead, in
particular without knowledge of the optimal solution. In our numerical
experiments, this guaranteed errors of at most a few percent.
