arXiv (All)
open access
An $\tilde Ω(\log n \log m)$ Information-Theoretic Lower Bound for Randomized Online Set Cover
We show an information-theoretic lower bound of $Ω\left(\frac{\log n \log m}{\log \log n + \log \log m}\right)$ for online set cover against randomized algorithms, for all sufficiently large $m$ and $n$ satisfying $\log^2 n \leq m \leq 2^n$.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Tight $\widetilde Ω(\sqrt{m})$ Information-Theoretic Lower Bound for Randomized Online Set Cover#1000149
- Local large deviations for triangles in sparse random graphs#1036279
- Informational Self-Determination: A Convincing Rationale for Data Protection Law?#461878
- Concentration Inequalities for Branching Random Walks with Applications to Phase Transitions in CSPs#793343
- Concentration Inequalities for Branching Random Walks with Applications to Phase Transitions in CSPs#789339
Record · ID 1034420
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive