A Tight $\widetilde Ω(\sqrt{m})$ Information-Theoretic Lower Bound for Randomized Online Set Cover

Abstract

Online set cover is a fundamental problem in online algorithms, admitting a deterministic $O(\log m\log n)$-competitive algorithm, where $m$ is the number of sets and $n$ is the number of elements. This is essentially tight for deterministic algorithms as well as for polynomial-time randomized algorithms assuming $\mathrm{NP}\not\subseteq\mathrm{BPP}$. However, the best lower bound known for information-theoretic (computationally unlimited) randomized algorithms against an oblivious adversary is only $Ω(\log m)$, whereas the upper bound in terms of $m$ is $O(\sqrt m\log m)=\widetilde O(\sqrt m)$. We prove an $Ω(\sqrt m)$ lower bound for information-theoretic randomized unweighted online set cover, showing that even with unbounded computational power, randomization cannot achieve an $O(\log m)$-competitive ratio. Specifically, our lower bound rules out $O(\log m\cdot\log^{1/2-\varepsilon} n)$-competitive algorithms for every constant $\varepsilon>0$. Our techniques also prove an $Ω(m^{1/3})$ lower bound in the random-order model, and show that every algorithm with $poly(m)$ memory has competitive ratio $Ω(m/\log m)$, even with unlimited computation between requests.