A Lossless Deamortization for Dynamic Greedy Set Cover

TL;DR

This paper introduces a new lossless deamortization technique for dynamic greedy set cover algorithms, achieving worst-case update times comparable to previous amortized bounds and improving approximation guarantees.

Contribution

It presents the first efficient deamortization of a greedy set cover algorithm, providing worst-case update times that match or improve previous amortized bounds.

Findings

Achieves $((1+psilon) ln n)$-approximation with $O(rac{f g }{psilon^2})$ worst-case update time.

Improves the psilon-dependence of previous bounds.

Demonstrates applicability by enhancing existing primal-dual algorithms with worst-case guarantees.

Abstract

The dynamic set cover problem has been subject to growing research attention in recent years. In this problem, we are given as input a dynamic universe of at most $n$ elements and a fixed collection of $m$ sets, where each element appears in a most $f$ sets and the cost of each set is in $[1/C, 1]$, and the goal is to efficiently maintain an approximate minimum set cover under element updates. Two algorithms that dynamize the classic greedy algorithm are known, providing $O(\log n)$ and $((1+\epsilon)\ln n)$-approximation with amortized update times $O(f \log n)$ and $O(\frac{f \log n}{\epsilon^5})$, respectively [GKKP (STOC'17); SU (STOC'23)]. The question of whether one can get approximation $O(\log n)$ (or even worse) with low worst-case update time has remained open -- only the naive $O(f \cdot n)$ time bound is known, even for unweighted instances. In this work we devise the first amortized greedy algorithm that is amenable to an efficient deamortization, and also develop a lossless deamortization approach suitable for the set cover problem, the combination of which yields a $((1+\epsilon)\ln n)$-approximation algorithm with a worst-case update time of $O(\frac{f\log n}{\epsilon^2})$. Our worst-case time bound -- the first to break the naive $O(f \cdot n)$ bound -- matches the previous best amortized bound, and actually improves its $\epsilon$-dependence. Further, to demonstrate the applicability of our deamortization approach, we employ it, in conjunction with the primal-dual amortized algorithm of [BHN (FOCS'19)], to obtain a $((1+\epsilon)f)$-approximation algorithm with a worst-case update time of $O(\frac{f\log n}{\epsilon^2})$, improving over the previous best bound of $O(\frac{f \cdot \log^2(Cn)}{\epsilon^3})$ [BHNW (SODA'21)]. Finally, as direct implications of our results for set cover, we [...]