A Lossless Deamortization for Dynamic Greedy Set Cover

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+ϵ)\ln n)-approximation with amortized update times O(f log n) and, O(\frac{f log n}{ϵ}), 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· 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+ϵ) n)- approximation algorithm with a worst-case update time of O(\frac{f log n}{ϵ²}). Our worst-case time bound - the first to break the naive O(f· n) bound - matches the previous best amortized bound, and actually improves its ϵ -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+ϵ)f)-approximation algorithm with a worst-case update time of O(\frac{f log n}{ϵ²}), improving over the previous best bound of O(\frac{f · log ²(C n)}{-3})\ [ BHNW (SODA'21)]. Finally, as direct implications of our results for set cover, we (i) achieve the first nontrivial worst-case update time for the dominating set problem, and (ii) improve the state-of-the-art worst-case update time for the vertex cover problem.