Improved Local Computation Algorithms for Greedy Set Cover via Retroactive Updates
This work addresses the computational efficiency of set cover algorithms for applications in distributed or local computation settings, representing an incremental improvement over prior methods.
The paper tackles the problem of designing an efficient Local Computation Algorithm (LCA) for the set cover problem, achieving a query complexity of f^{O(log Δ)} and improving it to Δ^{O(log log Δ)} for instances where f = poly log Δ, compared to the previous state-of-the-art.
In this work, we focus on designing an efficient Local Computation Algorithm (LCA) for the set cover problem, which is a core optimization task. The state-of-the-art LCA for computing $O(\log Î)$-approximate set cover, developed by Grunau, MitroviÄ, Rubinfeld, and Vakilian [SODA '20], achieves query complexity of $Î^{O(\log Î)} \cdot f^{O(\log Î\cdot (\log \log Î+ \log \log f))}$, where $Î$ is the maximum set size, and $f$ is the maximum frequency of any element in sets. We present a new LCA that solves this problem using $f^{O(\log Î)}$ queries. Specifically, for instances where $f = \text{poly} \log Î$, our algorithm improves the query complexity from $Î^{O(\log Î)}$ to $Î^{O(\log \log Î)}$. Our central technical contribution in designing LCAs is to aggressively sparsify the input instance but to allow for \emph{retroactive updates}. Namely, our main LCA sometimes ``corrects'' decisions it made in the previous recursive LCA calls. It enables us to achieve stronger concentration guarantees, which in turn allows for more efficient and ``sparser'' LCA execution. We believe that this technique will be of independent interest.