Dynamic graph algorithms have seen significant theoretical advancements, but
practical evaluations often lag behind. This work bridges the gap between
theory and practice by engineering and empirically evaluating recently
developed approximation algorithms for dynamically maintaining graph
orientations. We comprehensively describe the underlying data structures,
including efficient bucketing techniques and round-robin updates. Our
implementation has a natural parameter $\lambda$, which allows for a trade-off
between algorithmic efficiency and the quality of the solution. In the
extensive experimental evaluation, we demonstrate that our implementation
offers a considerable speedup. Using different quality metrics, we show that
our implementations are very competitive and can outperform previous methods.
Overall, our approach solves more instances than other methods while being up
to 112 times faster on instances that are solvable by all methods compared.

Este artículo explora los viajes en el tiempo y sus implicaciones.

Descargar PDF: