no code implementations • 16 May 2023 • Thomas Erlebach, Murilo Santos de Lima, Nicole Megow, Jens Schlöter
We study learning-augmented algorithms for sorting and hypergraph orientation under uncertainty, assuming access to untrusted predictions for the uncertain values.
no code implementations • 13 Jan 2021 • Thomas Erlebach, Michael Hoffmann, Murilo S. de Lima
Given a set of uncertain elements and a family of $m$ subsets of that set, we present an algorithm for determining the value of the minimum of each of the subsets that requires at most $(2+\varepsilon) \cdot \mathrm{opt}_k+\mathrm{O}\left(\frac{1}{\varepsilon} \cdot \lg m\right)$ rounds for every $0<\varepsilon<1$, where $\mathrm{opt}_k$ is the optimal number of rounds, as well as nearly matching lower bounds.
Data Structures and Algorithms
